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Crop Pest Target Detection Algorithm in Complex Scenes:YOLOv8-Extend ZHANG Ronghua, BAI Xue, FAN Jiangchuan Smart Agriculture    2024, 6 (2): 49-61.   DOI: 10.12133/j.smartag.SA202311007 Abstract （1897）   HTML （194）    PDF（pc） （2287KB）（95218）       Knowledge map    Save [Objective] It is of great significance to improve the efficiency and accuracy of crop pest detection in complex natural environments, and to change the current reliance on expert manual identification in the agricultural production process. Targeting the problems of small target size, mimicry with crops, low detection accuracy, and slow algorithm reasoning speed in crop pest detection, a complex scene crop pest target detection algorithm named YOLOv8-Entend was proposed in this research. [Methods] Firstly, the GSConv was introduecd to enhance the model's receptive field, allowing for global feature aggregation. This mechanism enables feature aggregation at both node and global levels simultaneously, obtaining local features from neighboring nodes through neighbor sampling and aggregation operations, enhancing the model's receptive field and semantic understanding ability. Additionally, some Convs were replaced with lightweight Ghost Convolutions and HorBlock was utilized to capture longer-term feature dependencies. The recursive gate convolution employed gating mechanisms to remember and transmit previous information, capturing long-term correlations. Furthermore, Concat was replaced with BiFPN for richer feature fusion. The bidirectional fusion of depth features from top to bottom and from bottom to top enhances the transmission of feature information acrossed different network layers. Utilizing the VoVGSCSP module, feature maps of different scales were connected to create longer feature map vectors, increasing model diversity and enhancing small object detection. The convolutional block attention module (CBAM) attention mechanism was introduced to strengthen features of field pests and reduce background weights caused by complexity. Next, the Wise IoU dynamic non-monotonic focusing mechanism was implemented to evaluate the quality of anchor boxes using "outlier" instead of IoU. This mechanism also included a gradient gain allocation strategy, which reduced the competitiveness of high-quality anchor frames and minimizes harmful gradients from low-quality examples. This approach allowed WIoU to concentrate on anchor boxes of average quality, improving the network model's generalization ability and overall performance. Subsequently, the improved YOLOv8-Extend model was compared with the original YOLOv8 model, YOLOv5, YOLOv8-GSCONV, YOLOv8-BiFPN, and YOLOv8-CBAM to validate the accuracy and precision of model detection. Finally, the model was deployed on edge devices for inference verification to confirm its effectiveness in practical application scenarios. [Results and Discussions] The results indicated that the improved YOLOv8-Extend model achieved notable improvements in accuracy, recall, mAP@0.5, and mAP@0.5:0.95 evaluation indices. Specifically, there were increases of 2.6%, 3.6%, 2.4% and 7.2%, respectively, showcasing superior detection performance. YOLOv8-Extend and YOLOv8 run respectively on the edge computing device JETSON ORIN NX 16 GB and were accelerated by TensorRT, mAP@0.5 improved by 4.6%, FPS reached 57.6, meeting real-time detection requirements. The YOLOv8-Extend model demonstrated better adaptability in complex agricultural scenarios and exhibited clear advantages in detecting small pests and pests sharing similar growth environments in practical data collection. The accuracy in detecting challenging data saw a notable increased of 11.9%. Through algorithm refinement, the model showcased improved capability in extracting and focusing on features in crop pest target detection, addressing issues such as small targets, similar background textures, and challenging feature extraction. [Conclusions] The YOLOv8-Extend model introduced in this study significantly boosts detection accuracy and recognition rates while upholding high operational efficiency. It is suitable for deployment on edge terminal computing devices to facilitate real-time detection of crop pests, offering technological advancements and methodologies for the advancement of cost-effective terminal-based automatic pest recognition systems. This research can serve as a valuable resource and aid in the intelligent detection of other small targets, as well as in optimizing model structures. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Lightweighted Wheat Leaf Diseases and Pests Detection Model Based on Improved YOLOv8 YANG Feng, YAO Xiaotong Smart Agriculture    2024, 6 (1): 147-157.   DOI: 10.12133/j.smartag.SA202309010 Abstract （1612）   HTML （254）    PDF（pc） （1991KB）（63881）       Knowledge map    Save Objective To effectively tackle the unique attributes of wheat leaf pests and diseases in their native environment, a high-caliber and efficient pest detection model named YOLOv8-SS (You Only Look Once Version 8-SS) was proposed. This innovative model is engineered to accurately identify pests, thereby providing a solid scientific foundation for their prevention and management strategies. Methods A total of 3 639 raw datasets of images of wheat leaf pests and diseases were collected from 6 different wheat pests and diseases in various farmlands in the Yuchong County area of Gansu Province, at different periods of time, using mobile phones. This collection demonstrated the team's proficiency and commitment to advancing agricultural research. The dataset was meticulously constructed using the LabelImg software to accurately label the images with targeted pest species. To guarantee the model's superior generalization capabilities, the dataset was strategically divided into a training set and a test set in an 8:2 ratio. The dataset includes thorough observations and recordings of the wheat leaf blade's appearance, texture, color, as well as other variables that could influence these characteristics. The compiled dataset proved to be an invaluable asset for both training and validation activities. Leveraging the YOLOv8 algorithm, an enhanced lightweight convolutional neural network, ShuffleNetv2, was selected as the basis network for feature extraction from images. This was accomplished by integrating a 3×3 Depthwise Convolution (DWConv) kernel, the h-swish activation function, and a Squeeze-and-Excitation Network (SENet) attention mechanism. These enhancements streamlined the model by diminishing the parameter count and computational demands, all while sustaining high detection precision. The deployment of these sophisticated methodologies exemplified the researchers' commitment and passion for innovation. The YOLOv8 model employs the SEnet attention mechanism module within both its Backbone and Neck components, significantly reducing computational load while bolstering accuracy. This method exemplifies the model's exceptional performance, distinguishing it from other models in the domain. By integrating a dedicated small target detection layer, the model's capabilities have been augmented, enabling more efficient and precise pest and disease detection. The introduction of a new detection feature map, sized 160×160 pixels, enables the network to concentrate on identifying small-targeted pests and diseases, thereby enhancing the accuracy of pest and disease recognition. Results and Discussion The YOLOv8-SS wheat leaf pests and diseases detection model has been significantly improved to accurately detect wheat leaf pests and diseases in their natural environment. By employing the refined ShuffleNet V2 within the DarkNet-53 framework, as opposed to the conventional YOLOv8, under identical experimental settings, the model exhibited a 4.53% increase in recognition accuracy and a 4.91% improvement in F1-Score, compared to the initial model. Furthermore, the incorporation of a dedicated small target detection layer led to a subsequent rise in accuracy and F1-Scores of 2.31% and 2.16%, respectively, despite a minimal upsurge in the number of parameters and computational requirements. The integration of the SEnet attention mechanism module into the YOLOv8 model resulted in a detection accuracy rate increase of 1.85% and an F1-Score enhancement of 2.72%. Furthermore, by swapping the original neural network architecture with an enhanced ShuffleNet V2 and appending a compact object detection sublayer (namely YOLOv8-SS), the resulting model exhibited a heightened recognition accuracy of 89.41% and an F1-Score of 88.12%. The YOLOv8-SS variant substantially outperformed the standard YOLOv8, showing a remarkable enhancement of 10.11% and 9.92% in accuracy, respectively. This outcome strikingly illustrates the YOLOv8-SS's prowess in balancing speed with precision. Moreover, it achieves convergence at a more rapid pace, requiring approximately 40 training epochs, to surpass other renowned models such as Faster R-CNN, MobileNetV2, SSD, YOLOv5, YOLOX, and the original YOLOv8 in accuracy. Specifically, the YOLOv8-SS boasted an average accuracy 23.01%, 15.13%, 11%, 25.21%, 27.52%, and 10.11% greater than that of the competing models, respectively. In a head-to-head trial involving a public dataset (LWDCD 2020) and a custom-built dataset, the LWDCD 2020 dataset yielded a striking accuracy of 91.30%, outperforming the custom-built dataset by a margin of 1.89% when utilizing the same network architecture, YOLOv8-SS. The AI Challenger 2018-6 and Plant-Village-5 datasets did not perform as robustly, achieving accuracy rates of 86.90% and 86.78% respectively. The YOLOv8-SS model has shown substantial improvements in both feature extraction and learning capabilities over the original YOLOv8, particularly excelling in natural environments with intricate, unstructured backdrops. Conclusion The YOLOv8-SS model is meticulously designed to deliver unmatched recognition accuracy while consuming a minimal amount of storage space. In contrast to conventional detection models, this groundbreaking model exhibits superior detection accuracy and speed, rendering it exceedingly valuable across various applications. This breakthrough serves as an invaluable resource for cutting-edge research on crop pest and disease detection within natural environments featuring complex, unstructured backgrounds. Our method is versatile and yields significantly enhanced detection performance, all while maintaining a lean model architecture. This renders it highly appropriate for real-world scenarios involving large-scale crop pest and disease detection. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Knowledge Intelligent Service Technology: A Review ZHAO Chunjiang Smart Agriculture    2023, 5 (2): 126-148.   DOI: 10.12133/j.smartag.SA202306002 Abstract （2545）   HTML （484）    PDF（pc） （3579KB）（49120）       Knowledge map    Save Significance Agricultural environment is dynamic and variable, with numerous factors affecting the growth of animals and plants and complex interactions. There are numerous factors that affect the growth of all kinds of animals and plants. There is a close but complex correlation between these factors such as air temperature, air humidity, illumination, soil temperature, soil humidity, diseases, pests, weeds and etc. Thus, farmers need agricultural knowledge to solve production problems. With the rapid development of internet technology, a vast amount of agricultural information and knowledge is available on the internet. However, due to the lack of effective organization, the utilization rate of these agricultural information knowledge is relatively low.How to analyze and generate production knowledge or decision cases from scattered and disordered information is a big challenge all over the world. Agricultural knowledge intelligent service technology is a good way to resolve the agricultural data problems such as low rank, low correlation, and poor interpretability of reasoning. It is also the key technology to improving the comprehensive prediction and decision-making analysis capabilities of the entire agricultural production process. It can eliminate the information barriers between agricultural knowledge, farmers, and consumers, and is more conducive to improve the production and quality of agricultural products, provide effective information services. Progress The definition, scope, and technical application of agricultural knowledge intelligence services are introduced in this paper. The demand for agricultural knowledge services are analyzed combining with artificial intelligence technology. Agricultural knowledge intelligent service technologies such as perceptual recognition, knowledge coupling, and inference decision-making are conducted. The characteristics of agricultural knowledge services are analyzed and summarized from multiple perspectives such as industrial demand, industrial upgrading, and technological development. The development history of agricultural knowledge services is introduced. Current problems and future trends are also discussed in the agricultural knowledge services field. Key issues in agricultural knowledge intelligence services such as animal and plant state recognition in complex and uncertain environments, multimodal data association knowledge extraction, and collaborative reasoning in multiple agricultural application scenarios have been discussed. Combining practical experience and theoretical research, a set of intelligent agricultural situation analysis service framework that covers the entire life cycle of agricultural animals and plants and combines knowledge cases is proposed. An agricultural situation perception framework has been built based on satellite air ground multi-channel perception platform and Internet real-time data. Multimodal knowledge coupling, multimodal knowledge graph construction and natural language processing technology have been used to converge and manage agricultural big data. Through knowledge reasoning decision-making, agricultural information mining and early warning have been carried out to provide users with multi-scenario agricultural knowledge services. Intelligent agricultural knowledge services have been designed such as multimodal fusion feature extraction, cross domain knowledge unified representation and graph construction, and complex and uncertain agricultural reasoning and decision-making. An agricultural knowledge intelligent service platform composed of cloud computing support environment, big data processing framework, knowledge organization management tools, and knowledge service application scenarios has been built. Rapid assembly and configuration management of agricultural knowledge services could be provide by the platform. The application threshold of artificial intelligence technology in agricultural knowledge services could be reduced. In this case, problems of agricultural users can be solved. A novel method for agricultural situation analysis and production decision-making is proposed. A full chain of intelligent knowledge application scenario is constructed. The scenarios include planning, management, harvest and operations during the agricultural before, during and after the whole process. Conclusions and Prospects The technology trend of agricultural knowledge intelligent service is summarized in five aspects. (1) Multi-scale sparse feature discovery and spatiotemporal situation recognition of agricultural conditions. The application effects of small sample migration discovery and target tracking in uncertain agricultural information acquisition and situation recognition are discussed. (2) The construction and self-evolution of agricultural cross media knowledge graph, which uses robust knowledge base and knowledge graph to analyze and gather high-level semantic information of cross media content. (3) In response to the difficulties in tracing the origin of complex agricultural conditions and the low accuracy of comprehensive prediction, multi granularity correlation and multi-mode collaborative inversion prediction of complex agricultural conditions is discussed. (4) The large language model (LLM) in the agricultural field based on generative artificial intelligence. ChatGPT and other LLMs can accurately mine agricultural data and automatically generate questions through large-scale computing power, solving the problems of user intention understanding and precise service under conditions of dispersed agricultural data, multi-source heterogeneity, high noise, low information density, and strong uncertainty. In addition, the agricultural LLM can also significantly improve the accuracy of intelligent algorithms such as identification, prediction and decision-making by combining strong algorithms with Big data and super computing power. These could bring important opportunities for large-scale intelligent agricultural production. (5) The construction of knowledge intelligence service platforms and new paradigm of knowledge service, integrating and innovating a self-evolving agricultural knowledge intelligence service cloud platform. Agricultural knowledge intelligent service technology will enhance the control ability of the whole agricultural production chain. It plays a technical support role in achieving the transformation of agricultural production from "observing the sky and working" to "knowing the sky and working". The intelligent agricultural application model of "knowledge empowerment" provides strong support for improving the quality and efficiency of the agricultural industry, as well as for the modernization transformation and upgrading. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Identifying Multiple Apple Leaf Diseases Based on the Improved CBAM-ResNet18 Model Under Weak Supervision ZHANG Wenjing, JIANG Zezhong, QIN Lifeng Smart Agriculture    2023, 5 (1): 111-121.   DOI: 10.12133/j.smartag.SA202301005 Abstract （1242）   HTML （158）    PDF（pc） （1490KB）（25130）       Knowledge map    Save To deal with the issues of low accuracy of apple leaf disease images recognition under weak supervision with only image category labeling, an improved CBAM-ResNet-based algorithm was proposed in this research. Using ResNet18 as the base model, the multilayer perceptron (MLP) in the lightweight convolutional block attention module (CBAM) attention mechanism channel was improved by up-dimensioning to amplify the details of apple leaf disease features. The improved CBAM attention module was incorporated into the residual module to enhance the key details of AlphaDropout with SeLU (Scaled Exponential Linearunits) to prevent overfitting of its network and accelerate the convergence effect of the model. Finally, the learning rate was adjusted using a single-cycle cosine annealing algorithm to obtain the disease recognition model. The training test was performed under weak supervision with only image-level annotation of all sample images, which greatly reduced the annotation cost. Through ablation experiments, the best dimensional improvement of MLP in CBAM was explored as 2. Compared with the original CBAM, the accuracy rate was increased by 0.32%, and the training time of each round was reduced by 8 s when the number of parameters increased by 17.59%. Tests were conducted on a dataset of 6185 images containing five diseases, including apple spotted leaf drop, brown spot, mosaic, gray spot, and rust, and the results showed that the model achieved an average recognition accuracy of 98.44% for the five apple diseases under weakly supervised learning. The improved CBAM-ResNet18 had increased by 1.47% compared with the pre-improved ResNet18, and was higher than VGG16, DesNet121, ResNet50, ResNeXt50, EfficientNet-B0 and Xception control model. In terms of learning efficiency, the improved CBAM-ResNet18 compared to ResNet18 reduced the training time of each round by 6 s under the condition that the number of parameters increased by 24.9%, and completed model training at the fastest speed of 137 s per round in VGG16, DesNet121, ResNet50, ResNeXt50, Efficient Net-B0 and Xception control models. Through the results of the confusion matrix, the average precision, average recall rate, and average F1 score of the model were calculated to reach 98.43%, 98.46%, and 0.9845, respectively. The results showed that the proposed improved CBAM-ResNet18 model could perform apple leaf disease identification and had good identification results, and could provide technical support for intelligent apple leaf disease identification providing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Sensor: Research Progress, Challenges and Perspectives WANG Rujing Smart Agriculture    2024, 6 (1): 1-17.   DOI: 10.12133/j.smartag.SA202401017 Abstract （2187）   HTML （441）    PDF（pc） （1179KB）（23271）       Knowledge map    Save Significance Agricultural sensor is the key technology for developing modern agriculture. Agricultural sensor is a kind of detection device that can sense and convert physical signal, which is related to the agricultural environment, plants and animals, into an electrical signal. Agricultural sensors could be applied to monitor crops and livestock in different agricultural environments, including weather, water, atmosphere and soil. It is also an important driving force to promote the iterative upgrading of agricultural technology and change agricultural production methods. Progress The different agricultural sensors are categorized, the cutting-edge research trends of agricultural sensors are analyzed, and summarizes the current research status of agricultural sensors are summarized in different application scenarios. Moreover, a deep analysis and discussion of four major categories is conducted, which include agricultural environment sensors, animal and plant life information sensors, agricultural product quality and safety sensors, and agricultural machinery sensors. The process of research, development, the universality and limitations of the application of the four types of agricultural sensors are summarized. Agricultural environment sensors are mainly used for real-time monitoring of key parameters in agricultural production environments, such as the quality of water, gas, and soil. The soil sensors provide data support for precision irrigation, rational fertilization, and soil management by monitoring indicators such as soil humidity, pH, temperature, nutrients, microorganisms, pests and diseases, heavy metals and agricultural pollution, etc. Monitoring of dissolved oxygen, pH, nitrate content, and organophosphorus pesticides in irrigation and aquaculture water through water sensors ensures the rational use of water resources and water quality safety. The gas sensor monitors the atmospheric CO2, NH3, C2H2, CH4 concentration, and other information, which provides the appropriate environmental conditions for the growth of crops in greenhouses. The animal life information sensor can obtain the animal's growth, movement, physiological and biochemical status, which include movement trajectory, food intake, heart rate, body temperature, blood pressure, blood glucose, etc. The plant life information sensors monitor the plant's health and growth, such as volatile organic compounds of the leaves, surface temperature and humidity, phytohormones, and other parameters. Especially, the flexible wearable plant sensors provide a new way to measure plant physiological characteristics accurately and monitor the water status and physiological activities of plants non-destructively and continuously. These sensors are mainly used to detect various indicators in agricultural products, such as temperature and humidity, freshness, nutrients, and potentially hazardous substances (e.g., bacteria, pesticide residues, heavy metals, etc. Agricultural machinery sensors can achieve real-time monitoring and controlling of agricultural machinery to achieve real-time cultivation, planting, management, and harvesting, automated operation of agricultural machinery, and accurate application of pesticide, fertilizer. [Conclusions and Prospects In the challenges and prospects of agricultural sensors, the core bottlenecks of large-scale application of agricultural sensors at the present stage are analyzed in detail. These include low-cost, specialization, high stability, and adaptive intelligence of agricultural sensors. Furthermore, the concept of "ubiquitous sensing in agriculture" is proposed, which provides ideas and references for the research and development of agricultural sensor technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Shrimp Diseases Detection Method Based on Improved YOLOv8 and Multiple Features XU Ruifeng, WANG Yaohua, DING Wenyong, YU Junqi, YAN Maocang, CHEN Chen Smart Agriculture    2024, 6 (2): 62-71.   DOI: 10.12133/j.smartag.SA201311014 Abstract （1625）   HTML （62）    PDF（pc） （1597KB）（19374）       Knowledge map    Save [Objective] In recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases. [Methods] To address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health. [Results and Discussions] The improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality. [Conclusions] The multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods. 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Review on Energy Efficiency Assessment and Carbon Emission Accounting of Food Cold Chain WANG Xiang, ZOU Jingui, LI You, SUN Yun, ZHANG Xiaoshuan Smart Agriculture    2023, 5 (1): 1-21.   DOI: 10.12133/j.smartag.SA202301007 Abstract （978）   HTML （184）    PDF（pc） （1296KB）（18593）       Knowledge map    Save The global energy is increasingly tight, and the global temperature is gradually rising. Energy efficiency assessment and carbon emission accounting can provide theoretical tools and practical support for the formulation of energy conservation and emission reduction strategies for the food cold chain, and is also a prerequisite for the sustainable development of the food cold chain. In this paper, the relationship and differences between energy consumption and carbon emissions in the general food cold chain are first described, and the principle, advantages and disadvantages of three energy consumption conversion standards of solar emergy value, standard coal and equivalent electricity are discussed. Besides, the possibilities of applying these three energy consumption conversion standards to energy consumption analysis and energy efficiency evaluation of food cold chain are explored. Then, for a batch of fresh agricultural products, the energy consumption of six links of the food cold chain, including the first transportation, the manufacturer, the second transportation, the distribution center, the third transportation, and the retailer, are systematically and comprehensively analyzed from the product level, and the comprehensive energy consumption level of the food cold chain are obtained. On this basis, ten energy efficiency indicators from five aspects of macro energy efficiency are proposed, including micro energy efficiency, energy economy, environmental energy efficiency and comprehensive energy efficiency, and constructs the energy efficiency evaluation index system of food cold chain. At the same time, other energy efficiency evaluation indicators and methods are also summarized. In addition, the standard of carbon emission conversion of food cold chain, namely carbon dioxide equivalent is introduce, the boundary of carbon emission accounting is determined, and the carbon emission factors of China's electricity is mainly discussed. Furthermore, the origin, principle, advantages and disadvantages of the emission factor method, the life cycle assessment method, the input-output analysis method and the hybrid life cycle assessment method, and the basic process of life cycle assessment method in the calculation of food cold chain carbon footprint are also reviewed. In order to improve the energy efficiency level of the food cold chain and reduce the carbon emissions of each link of the food cold chain, energy conservation and emission reduction methods for food cold chain are proposed from five aspects: refrigerant, distribution path, energy, phase change cool storage technology and digital twin technology. Finally, the energy efficiency assessment and carbon emission accounting of the food cold chain are briefly prospected in order to provide reference for promoting the sustainable development of China's food cold chain. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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In Situ Identification Method of Maize Stalk Width Based on Binocular Vision and Improved YOLOv8 ZUO Haoxuan, HUANG Qicheng, YANG Jiahao, MENG Fanjia, LI Sien, LI Li Smart Agriculture    2023, 5 (3): 86-95.   DOI: 10.12133/j.smartag.SA202309004 Abstract （698）   HTML （88）    PDF（pc） （1659KB）（18545）       Knowledge map    Save [Objective] The width of maize stalks is an important indicator affecting the lodging resistance of maize. The measurement of maize stalk width has many problems, such as cumbersome manual collection process and large errors in the accuracy of automatic equipment collection and recognition, and it is of great application value to study a method for in-situ detection and high-precision identification of maize stalk width. [Methods] The ZED2i binocular camera was used and fixed in the field to obtain real-time pictures from the left and right sides of maize stalks together. The picture acquisition system was based on the NVIDIA Jetson TX2 NX development board, which could achieve timed shooting of both sides view of the maize by setting up the program. A total of maize original images were collected and a dataset was established. In order to observe more features in the target area from the image and provide assistance to improve model training generalization ability, the original images were processed by five processing methods: image saturation, brightness, contrast, sharpness and horizontal flipping, and the dataset was expanded to 3500 images. YOLOv8 was used as the original model for identifying maize stalks from a complex background. The coordinate attention (CA) attention mechanism can bring huge gains to downstream tasks on the basis of lightweight networks, so that the attention block can capture long-distance relationships in one direction while retaining spatial information in the other direction, so that the position information can be saved in the generated attention map to focus on the area of interest and help the network locate the target better and more accurately. By adding the CA module multiple times, the CA module was fused with the C2f module in the original Backbone, and the Bottleneck in the original C2f module was replaced by the CA module, and the C2fCA network module was redesigned. Replacing the loss function Efficient IoU Loss(EIoU) splits the loss term of the aspect ratio into the difference between the predicted width and height and the width and height of the minimum outer frame, which accelerated the convergence of the prediction box, improved the regression accuracy of the prediction box, and further improved the recognition accuracy of maize stalks. The binocular camera was then calibrated so that the left and right cameras were on the same three-dimensional plane. Then the three-dimensional reconstruction of maize stalks, and the matching of left and right cameras recognition frames was realized through the algorithm, first determine whether the detection number of recognition frames in the two images was equal, if not, re-enter the binocular image. If they were equal, continue to judge the coordinate information of the left and right images, the width and height of the bounding box, and determine whether the difference was less than the given Ta. If greater than the given Ta, the image was re-imported; If it was less than the given Ta, the confidence level of the recognition frame of the image was determined whether it was less than the given Tb. If greater than the given Tb, the image is re-imported; If it is less than the given Tb, it indicates that the recognition frame is the same maize identified in the left and right images. If the above conditions were met, the corresponding point matching in the binocular image was completed. After the three-dimensional reconstruction of the binocular image, the three-dimensional coordinates (Ax, Ay, Az) and (Bx, By, Bz) in the upper left and upper right corners of the recognition box under the world coordinate system were obtained, and the distance between the two points was the width of the maize stalk. Finally, a comparative analysis was conducted among the improved YOLOv8 model, the original YOLOv8 model, faster region convolutional neural networks (Faster R-CNN), and single shot multiBox detector (SSD)to verify the recognition accuracy and recognition accuracy of the model. [Results and Discussions] The precision rate (P)、recall rate (R)、average accuracy mAP0.5、average accuracy mAP0.5:0.95 of the improved YOLOv8 model reached 96.8%、94.1%、96.6% and 77.0%. Compared with YOLOv7, increased by 1.3%、1.3%、1.0% and 11.6%, compared with YOLOv5, increased by 1.8%、2.1%、1.2% and 15.8%, compared with Faster R-CNN, increased by 31.1%、40.3%、46.2%、and 37.6%, and compared with SSD, increased by 20.6%、23.8%、20.9% and 20.1%, respectively. Respectively, and the linear regression coefficient of determination R2, root mean square error RMSE and mean absolute error MAE were 0.373, 0.265 cm and 0.244 cm, respectively. The method proposed in the research can meet the requirements of actual production for the measurement accuracy of maize stalk width. [Conclusions] In this study, the in-situ recognition method of maize stalk width based on the improved YOLOv8 model can realize the accurate in-situ identification of maize stalks, which solves the problems of time-consuming and laborious manual measurement and poor machine vision recognition accuracy, and provides a theoretical basis for practical production applications. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Key Issues and Evaluation Methods for Constructing Agricultural Pest and Disease Image Datasets: A Review GUAN Bolun, ZHANG Liping, ZHU Jingbo, LI Runmei, KONG Juanjuan, WANG Yan, DONG Wei Smart Agriculture    2023, 5 (3): 17-34.   DOI: 10.12133/j.smartag.SA202306012 Abstract （1396）   HTML （179）    PDF（pc） （1576KB）（16101）       Knowledge map    Save [Significance] The scientific dataset of agricultural pests and diseases is the foundation for monitoring and warning of agricultural pests and diseases. It is of great significance for the development of agricultural pest control, and is an important component of developing smart agriculture. The quality of the dataset affecting the effectiveness of image recognition algorithms, with the discovery of the importance of deep learning technology in intelligent monitoring of agricultural pests and diseases. The construction of high-quality agricultural pest and disease datasets is gradually attracting attention from scholars in this field. In the task of image recognition, on one hand, the recognition effect depends on the improvement strategy of the algorithm, and on the other hand, it depends on the quality of the dataset. The same recognition algorithm learns different features in different quality datasets, so its recognition performance also varies. In order to propose a dataset evaluation index to measure the quality of agricultural pest and disease datasets, this article analyzes the existing datasets and takes the challenges faced in constructing agricultural pest and disease image datasets as the starting point to review the construction of agricultural pest and disease datasets. [Progress] Firstly, disease and pest datasets are divided into two categories: private datasets and public datasets. Private datasets have the characteristics of high annotation quality, high image quality, and a large number of inter class samples that are not publicly available. Public datasets have the characteristics of multiple types, low image quality, and poor annotation quality. Secondly, the problems faced in the construction process of datasets are summarized, including imbalanced categories at the dataset level, difficulty in feature extraction at the dataset sample level, and difficulty in measuring the dataset size at the usage level. These include imbalanced inter class and intra class samples, selection bias, multi-scale targets, dense targets, uneven data distribution, uneven image quality, insufficient dataset size, and dataset availability. The main reasons for the problem are analyzed by two key aspects of image acquisition and annotation methods in dataset construction, and the improvement strategies and suggestions for the algorithm to address the above issues are summarized. The collection devices of the dataset can be divided into handheld devices, drone platforms, and fixed collection devices. The collection method of handheld devices is flexible and convenient, but it is inefficient and requires high photography skills. The drone platform acquisition method is suitable for data collection in contiguous areas, but the detailed features captured are not clear enough. The fixed device acquisition method has higher efficiency, but the shooting scene is often relatively fixed. The annotation of image data is divided into rectangular annotation and polygonal annotation. In image recognition and detection, rectangular annotation is generally used more frequently. It is difficult to label images that are difficult to separate the target and background. Improper annotation can lead to the introduction of more noise or incomplete algorithm feature extraction. In response to the problems in the above three aspects, the evaluation methods are summarized for data distribution consistency, dataset size, and image annotation quality at the end of the article. [Conclusions and Prospects] The future research and development suggestions for constructing high-quality agricultural pest and disease image datasets based are proposed on the actual needs of agricultural pest and disease image recognition:(1) Construct agricultural pest and disease datasets combined with practical usage scenarios. In order to enable the algorithm to extract richer target features, image data can be collected from multiple perspectives and environments to construct a dataset. According to actual needs, data categories can be scientifically and reasonably divided from the perspective of algorithm feature extraction, avoiding unreasonable inter class and intra class distances, and thus constructing a dataset that meets task requirements for classification and balanced feature distribution. (2) Balancing the relationship between datasets and algorithms. When improving algorithms, consider the more sufficient distribution of categories and features in the dataset, as well as the size of the dataset that matches the model, to improve algorithm accuracy, robustness, and practicality. It ensures that comparative experiments are conducted on algorithm improvement under the same evaluation standard dataset, and improved the pest and disease image recognition algorithm. Research the correlation between the scale of agricultural pest and disease image data and algorithm performance, study the relationship between data annotation methods and algorithms that are difficult to annotate pest and disease images, integrate recognition algorithms for fuzzy, dense, occluded targets, and propose evaluation indicators for agricultural pest and disease datasets. (3) Enhancing the use value of datasets. Datasets can not only be used for research on image recognition, but also for research on other business needs. The identification, collection, and annotation of target images is a challenging task in the construction process of pest and disease datasets. In the process of collecting image data, in addition to collecting images, attention can be paid to the collection of surrounding environmental information and host information. This method is used to construct a multimodal agricultural pest and disease dataset, fully leveraging the value of the dataset. In order to focus researchers on business innovation research, it is necessary to innovate the organizational form of data collection, develop a big data platform for agricultural diseases and pests, explore the correlation between multimodal data, improve the accessibility and convenience of data, and provide efficient services for application implementation and business innovation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Application of Artificial Intelligence in Agricultural Risk Management: A Review GUI Zechun, ZHAO Sijian Smart Agriculture    2023, 5 (1): 82-98.   DOI: 10.12133/j.smartag.SA202211004 Abstract （1296）   HTML （146）    PDF（pc） （1410KB）（15981）       Knowledge map    Save Agriculture is a basic industry deeply related to the national economy and people's livelihood, while it is also a weak industry. There are some problems with traditional agricultural risk management research methods, such as insufficient mining of nonlinear information, low accuracy and poor robustness. Artificial intelligence(AI) has powerful functions such as strong nonlinear fitting, end-to-end modeling, feature self-learning based on big data, which can solve the above problems well. The research progress of artificial intelligence technology in agricultural vulnerability assessment, agricultural risk prediction and agricultural damage assessment were first analyzed in this paper, and the following conclusions were obtained: 1. The feature importance assessment of AI in agricultural vulnerability assessment lacks scientific and effective verification indicators, and the application method makes it impossible to compare the advantages and disadvantages of multiple AI models. Therefore, it is suggested to use subjective and objective methods for evaluation; 2. In risk prediction, it is found that with the increase of prediction time, the prediction ability of machine learning model tends to decline. Overfitting is a common problem in risk prediction, and there are few researches on the mining of spatial information of graph data; 3. Complex agricultural production environment and varied application scenarios are important factors affecting the accuracy of damage assessment. Improving the feature extraction ability and robustness of deep learning models is a key and difficult issue to be overcome in future technological development. Then, in view of the performance improvement problem and small sample problem existing in the application process of AI technology, corresponding solutions were put forward. For the performance improvement problem, according to the user's familiarity with artificial intelligence, a variety of model comparison method, model group method and neural network structure optimization method can be used respectively to improve the performance of the model; For the problem of small samples, data augmentation, GAN (Generative Adversarial Network) and transfer learning can often be combined to increase the amount of input data of the model, enhance the robustness of the model, accelerate the training speed of the model and improve the accuracy of model recognition. Finally, the applications of AI in agricultural risk management were prospected: In the future, AI algorithm could be considered in the construction of agricultural vulnerability curve; In view of the relationship between upstream and downstream of agricultural industry chain and agriculture-related industries, the graph neural network can be used more in the future to further study the agricultural price risk prediction; In the modeling process of future damage assessment, more professional knowledge related to the assessment target can be introduced to enhance the feature learning of the target, and expanding the small sample data is also the key subject of future research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Advances in the Applications of Deep Learning Technology for Livestock Smart Farming GUO Yangyang, DU Shuzeng, QIAO Yongliang, LIANG Dong Smart Agriculture    2023, 5 (1): 52-65.   DOI: 10.12133/j.smartag.SA202205009 Abstract （1655）   HTML （212）    PDF（pc） （1118KB）（14002）       Knowledge map    Save Accurate and efficient monitoring of animal information, timely analysis of animal physiological and physical health conditions, and automatic feeding and farming management combined with intelligent technologies are of great significance for large-scale livestock farming. Deep learning techniques, with automatic feature extraction and powerful image representation capabilities, solve many visual challenges, and are more suitable for application in monitoring animal information in complex livestock farming environments. In order to further analyze the research and application of artificial intelligence technology in intelligent animal farming, this paper presents the current state of research on deep learning techniques for tag detection recognition, body condition evaluation and weight estimation, and behavior recognition and quantitative analysis for cattle, sheep and pigs. Among them, target detection and recognition is conducive to the construction of electronic archives of individual animals, on which basis the body condition and weight information, behavior information and health status of animals can be related, which is also the trend of intelligent animal farming. At present, intelligent animal farming still faces many problems and challenges, such as the existence of multiple perspectives, multi-scale, multiple scenarios and even small sample size of a certain behavior in data samples, which greatly increases the detection difficulty and the generalization of intelligent technology application. In addition, animal breeding and animal habits are a long-term process. How to accurately monitor the animal health information in real time and effectively feed it back to the producer is also a technical difficulty. According to the actual feeding and management needs of animal farming, the development of intelligent animal farming is prospected and put forward. First, enrich the samples and build a multi perspective dataset, and combine semi supervised or small sample learning methods to improve the generalization ability of in-depth learning models, so as to realize the perception and analysis of the animal's physical environment. Secondly, the unified cooperation and harmonious development of human, intelligent equipment and breeding animals will improve the breeding efficiency and management level as a whole. Third, the deep integration of big data, deep learning technology and animal farming will greatly promote the development of intelligent animal farming. Last, research on the interpretability and security of artificial intelligence technology represented by deep learning model in the breeding field. And other development suggestions to further promote intelligent animal farming. Aiming at the progress of research application of deep learning in livestock smart farming, it provides reference for the modernization and intelligent development of livestock farming. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Lightweight Fruit Load Estimation Model for Edge Computing Equipment XIA Xue, CHAI Xiujuan, ZHANG Ning, ZHOU Shuo, SUN Qixin, SUN Tan Smart Agriculture    2023, 5 (2): 1-12.   DOI: 10.12133/j.smartag.SA202305004 Abstract （773）   HTML （157）    PDF（pc） （2277KB）（11090）       Knowledge map    Save [Objective] The fruit load estimation of fruit tree is essential for horticulture management. Traditional estimation method by manual sampling is not only labor-intensive and time-consuming but also prone to errors. Most existing models can not apply to edge computing equipment with limited computing resources because of their high model complexity. This study aims to develop a lightweight model for edge computing equipment to estimate fruit load automatically in the orchard. [Methods] The experimental data were captured using the smartphone in the citrus orchard in Jiangnan district, Nanning city, Guangxi province. In the dataset, 30 videos were randomly selected for model training and other 10 for testing. The general idea of the proposed algorithm was divided into two parts: Detecting fruits and extracting ReID features of fruits in each image from the video, then tracking fruit and estimating the fruit load. Specifically, the CSPDarknet53 network was used as the backbone of the model to achieve feature extraction as it consumes less hardware computing resources, which was suitable for edge computing equipment. The path aggregation feature pyramid network PAFPN was introduced as the neck part for the feature fusion via the jump connection between the low-level and high-level features. The fused features from the PAFPN were fed into two parallel branches. One was the fruit detection branch and another was the identity embedding branch. The fruit detection branch consisted of three prediction heads, each of which performed 3×3 convolution and 1×1 convolution on the feature map output by the PAFPN to predict the fruit's keypoint heat map, local offset and bounding box size, respectively. The identity embedding branch distinguished between different fruit identity features. In the fruit tracking stage, the byte mechanism from the ByteTrack algorithm was introduced to improve the data association of the FairMOT method, enhancing the performance of fruit load estimation in the video. The Byte algorithm considered both high-score and low-score detection boxes to associate the fruit motion trajectory, then matches the identity features' similarity of fruits between frames. The number of fruit IDs whose tracking duration longer than five frames was counted as the amount of citrus fruit in the video. [Results and Discussions] All experiments were conducted on edge computing equipment. The fruit detection experiment was conducted under the same test dataset containing 211 citrus tree images. The experimental results showed that applying CSPDarkNet53+PAFPN structure in the proposed model achieved a precision of 83.6%, recall of 89.2% and F1 score of 86.3%, respectively, which were superior to the same indexes of FairMOT (ResNet34) model, FairMOT (HRNet18) model and Faster RCNN model. The CSPDarkNet53+PAFPN structure adopted in the proposed model could better detect the fruits in the images, laying a foundation for estimating the amount of citrus fruit on trees. The model complexity experimental results showed that the number of parameters, FLOPs (Floating Point Operations) and size of the proposed model were 5.01 M, 36.44 G and 70.2 MB, respectively. The number of parameters for the proposed model was 20.19% of FairMOT (ResNet34) model's and 41.51% of FairMOT (HRNet18) model's. The FLOPs for the proposed model was 78.31% less than FairMOT (ResNet34) model's and 87.63% less than FairMOT (HRNet18) model's. The model size for the proposed model was 23.96% of FairMOT (ResNet34) model's and 45.00% of FairMOT (HRNet18) model's. Compared with the Faster RCNN, the model built in this study showed advantages in the number of parameters, FLOPs and model size. The low complexity proved that the proposed model was more friendly to edge computing equipment. Compared with the lightweight backbone network EfficientNet-Lite, the CSPDarkNet53 applied in the proposed model's backbone performed better fruit detection and model complexity. For fruit load estimation, the improved tracking strategy that integrated the Byte algorithm into the FairMOT positively boosted the estimation accuracy of fruit load. The experimental results on the test videos showed that the AEP (Average Estimating Precision) and FPS (Frames Per Second) of the proposed model reached 91.61% and 14.76 f/s, which indicated that the proposed model could maintain high estimation accuracy while the FPS was 2.4 times and 4.7 times of the comparison models, respectively. The RMSE (Root Mean Square Error) of the proposed model was 4.1713, which was 47.61% less than FairMOT (ResNet34) model's and 22.94% less than FairMOT (HRNet18) model's. The R2 of the determination coefficient between the algorithm-measured value and the manual counted value was 0.9858, which was superior to other comparison models. The proposed model revealed better performance in estimating fruit load and lower model complexity than other comparatives. [Conclusions] The experimental results proved the validity of the proposed model for fruit load estimation on edge computing equipment. This research could provide technical references for the automatic monitoring and analysis of orchard productivity. Future research will continue to enrich the data resources, further improve the model's performance, and explore more efficient methods to serve more fruit tree varieties. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing MA Yujing, WU Shangrong, YANG Peng, CAO Hong, TAN Jieyang, ZHAO Rongkun Smart Agriculture    2023, 5 (3): 1-16.   DOI: 10.12133/j.smartag.SA202303002 Abstract （966）   HTML （187）    PDF（pc） （837KB）（10441）       Knowledge map    Save [Significance] Oil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology. [Progress] This paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was expounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future. [Conclusions and Prospects] The paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estimation and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Detection of Pear Inflorescence Based on Improved Ghost-YOLOv5s-BiFPN Algorithm XIA Ye, LEI Xiaohui, QI Yannan, XU Tao, YUAN Quanchun, PAN Jian, JIANG Saike, LYU Xiaolan Smart Agriculture    2022, 4 (3): 108-119.   DOI: 10.12133/j.smartag.SA202207006 Abstract （959）   HTML （120）    PDF（pc） （2214KB）（10250）       Knowledge map    Save Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning. Reference | Related Articles | Metrics | Comments（0）

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Machine Learning Inversion Model of Soil Salinity in the Yellow River Delta Based on Field Hyperspectral and UAV Multispectral Data FAN Chengzhi, WANG Ziwen, YANG Xingchao, LUO Yongkai, XU Xuexin, GUO Bin, LI Zhenhai Smart Agriculture    2022, 4 (4): 61-73.   DOI: 10.12133/j.smartag.SA202212001 Abstract （1132）   HTML （78）    PDF（pc） （1831KB）（10209）       Knowledge map    Save Soil salinization in the Yellow River Delta is a difficult and miscellaneous disease to restrict the development of agricultural economy, and further hinders agricultural production. To explore the retrieval of soil salt content from remote sensing images under the condition of no vegetation coverage, the typical area of the Yellow River Delta was taken as the study area to obtain the hyperspectral of surface features, the multispectral of UAVs and the soil salt content of sample points. Three representative experimental areas with flat terrain and obvious soil salinization characteristics were set up in the study area, and 90 samples were collected in total. By optimizing the sensitive spectral parameters, machine learning algorithms of partial least squares regression (PLSR) and random forest (RF) for inversion of soil salt content were used in the study area. The results showed that: (1) Hyperspectral band of 1972 nm had the highest sensitivity to soil salt content, with correlation r of -0.31. The optimized spectral parameters of shortwave infrared can improve the accuracy of estimating soil salt content. (2) RF model optimized by two different data sources had better stability than PLSR model. RF model performed well in terms of generalization ability and balance error, but it had some over-fitting problems. (3) RF model based on ground feature hyperspectral (R2 =0.54, verified RMSE=3.30 g/kg) was superior to the random forest model based on UAV multispectral (R2 =0.54, verified RMSE=3.35 g/kg). The combination of image texture features improved the estimation accuracy of multispectral model, but the verification accuracy was still lower than that of hyperspectral model. (4) Soil salt content based on UAV multi-spectral imageries and RF model was mapped in the study area. This study demonstrates that the level of soil salinization in the Yellow River Delta region is significantly different in geographical location. The cultivated land in the study area is mainly light and moderate salinized soil with has certain restrictions on crop cultivation. Areas with low soil salt content are suitable for planting crops in low salinity fields, and farmland with high soil salt content is suitable for planting crops with high salinity tolerance. This study constructed and compared the soil salinity inversion models of the Yellow River Delta from two different sources of data, optimized them based on the advantages of each data source, explored the inversion of soil salinity content without vegetation coverage, and can provide a reference for more accurate inversion of land salinization. Reference | Related Articles | Metrics | Comments（0）

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Intelligent Identification of Crop Agronomic Traits and Morphological Structure Phenotypes: A Review ZHANG Jianhua, YAO Qiong, ZHOU Guomin, WU Wendi, XIU Xiaojie, WANG Jian Smart Agriculture    2024, 6 (2): 14-27.   DOI: 10.12133/j.smartag.SA202401015 Abstract （1847）   HTML （119）    PDF（pc） （1376KB）（10164）       Knowledge map    Save [Significance] The crop phenotype is the visible result of the complex interplay between crop genes and the environment. It reflects the physiological, ecological, and dynamic aspects of crop growth and development, serving as a critical component in the realm of advanced breeding techniques. By systematically analyzing crop phenotypes, researchers can gain valuable insights into gene function and identify genetic factors that influence important crop traits. This information can then be leveraged to effectively harness germplasm resources and develop breakthrough varieties. Utilizing data-driven, intelligent, dynamic, and non-invasive methods for measuring crop phenotypes allows researchers to accurately capture key growth traits and parameters, providing essential data for breeding and selecting superior crop varieties throughout the entire growth cycle. This article provides an overview of intelligent identification technologies for crop agronomic traits and morphological structural phenotypes. [Progress] Crop phenotype acquisition equipment serves as the essential foundation for acquiring, analyzing, measuring, and identifying crop phenotypes. This equipment enables detailed monitoring of crop growth status. The article presents an overview of the functions, performance, and applications of the leading high-throughput crop phenotyping platforms, as well as an analysis of the characteristics of various sensing and imaging devices used to obtain crop phenotypic information. The rapid advancement of high-throughput crop phenotyping platforms and sensory imaging equipment has facilitated the integration of cutting-edge imaging technology, spectroscopy technology, and deep learning algorithms. These technologies enable the automatic and high-throughput acquisition of yield, resistance, quality, and other relevant traits of large-scale crops, leading to the generation of extensive multi-dimensional, multi-scale, and multi-modal crop phenotypic data. This advancement supports the rapid progression of crop phenomics. The article also discusses the research progress of intelligent recognition technologies for agronomic traits such as crop plant height acquisition, crop organ detection, and counting, as well as crop ideotype recognition, crop morphological information measurement, and crop three-dimensional reconstruction for morphological structure intelligent recognition. Furthermore, this article outlines the main challenges faced in this field, including: difficulties in data collection in complex environments, high requirements for data scale, diversity, and preprocessing, the need to improve the lightweight nature and generalization ability of models, as well as the high cost of data collection equipment and the need to enhance practicality. [Conclusions and Prospects] Finally, this article puts forward the development directions of crop phenotype intelligent recognition technology, including: developing new and low cost intelligent field equipment for acquiring and analyzing crop phenotypes, enhancing the standardization and consistency of field crop phenotype acquisition, strengthening the generality of intelligent crop phenotype recognition models, researching crop phenotype recognition methods that involve multi-perspective, multimodal, multi-point continuous analysis, and spatiotemporal feature fusion, as well as improving model interpretability. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Advances and Prospects on Rapid Acquisition Technology of Farmland Soil Physical and Chemical Parameters QI Jiangtao, CHENG Panting, GAO Fangfang, GUO Li, ZHANG Ruirui Smart Agriculture    2024, 6 (3): 17-33.   DOI: 10.12133/j.smartag.SA202404003 Abstract （875）   HTML （123）    PDF（pc） （1524KB）（8787）       Knowledge map    Save [Significance] Soil stands as the fundamental pillar of agricultural production, with its quality being intrinsically linked to the efficiency and sustainability of farming practices. Historically, the intensive cultivation and soil erosion have led to a marked deterioration in some arable lands, characterized by a sharp decrease in soil organic matter, diminished fertility, and a decline in soil's structural integrity and ecological functions. In the strategic framework of safeguarding national food security and advancing the frontiers of smart and precision agriculture, the march towards agricultural modernization continues apace, intensifying the imperative for meticulous soil quality management. Consequently, there is an urgent need for the rrapid acquisition of soil's physical and chemical parameters. Interdisciplinary scholars have delved into soil monitoring research, achieving notable advancements that promise to revolutionize the way we understand and manage soil resource. [Progress] Utilizing the the Web of Science platform, a comprehensive literature search was conducted on the topic of "soil," further refined with supplementary keywords such as "electrochemistry", "spectroscopy", "electromagnetic", "ground-penetrating radar", and "satellite". The resulting literature was screened, synthesized, and imported into the CiteSpace visualization tool. A keyword emergence map was yielded, which delineates the trajectory of research in soil physical and chemical parameter detection technology. Analysis of the keyword emergence map reveals a paradigm shift in the acquisition of soil physical and chemical parameters, transitioning from conventional indoor chemical and spectrometry analyses to outdoor, real-time detection methods. Notably, soil sensors integrated into drones and satellites have garnered considerable interest. Additionally, emerging monitoring technologies, including biosensing and terahertz spectroscopy, have made their mark in recent years. Drawing from this analysis, the prevailing technologies for soil physical and chemical parameter information acquisition in agricultural fields have been categorized and summarized. These include: 1. Rapid Laboratory Testing Techniques: Primarily hinged on electrochemical and spectrometry analysis, these methods offer the dual benefits of time and resource efficiency alongside high precision; 2. Rapid Near-Ground Sensing Techniques: Leveraging electromagnetic induction, ground-penetrating radar, and various spectral sensors (multispectral, hyperspectral, and thermal infrared), these techniques are characterized by their high detection accuracy and swift operation. 3. Satellite Remote Sensing Techniques: Employing direct inversion, indirect inversion, and combined analysis methods, these approaches are prized for their efficiency and extensive coverage. 4. Innovative Rapid Acquisition Technologies: Stemming from interdisciplinary research, these include biosensing, environmental magnetism, terahertz spectroscopy, and gamma spectroscopy, each offering novel avenues for soil parameter detection. An in-depth examination and synthesis of the principles, applications, merits, and limitations of each technology have been provided. Moreover, a forward-looking perspective on the future trajectory of soil physical and chemical parameter acquisition technology has been offered, taking into account current research trends and hotspots. [Conclusions and Prospects] Current advancements in the technology for rapaid acquiring soil physical and chemical parameters in agricultural fields have been commendable, yet certain challenges persist. The development of near-ground monitoring sensors is constrained by cost, and their reliability, adaptability, and specialization require enhancement to effectively contend with the intricate and varied conditions of farmland environments. Additionally, remote sensing inversion techniques are confronted with existing limitations in data acquisition, processing, and application. To further develop the soil physical and chemical parameter acquisition technology and foster the evolution of smart agriculture, future research could beneficially delve into the following four areas: Designing portable, intelligent, and cost-effective near-ground soil information acquisition systems and equipment to facilitate rapid on-site soil information detection; Enhancing the performance of low-altitude soil information acquisition platforms and refine the methods for data interpretation to ensure more accurate insights; Integrating multifactorial considerations to construct robust satellite remote sensing inversion models, leveraging diverse and open cloud computing platforms for in-depth data analysis and mining; Engaging in thorough research on the fusion of multi-source data in the acquisition of soil physical and chemical parameter information, developing soil information sensing algorithms and models with strong generalizability and high reliability to achieve rapaid, precise, and intelligent acquisition of soil parameters. 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Research Progress and Prospect of Multi-robot Collaborative SLAM in Complex Agricultural Scenarios MA Nan, CAO Shanshan, BAI Tao, KONG Fantao, SUN Wei Smart Agriculture    2024, 6 (6): 23-43.   DOI: 10.12133/j.smartag.SA202406005 Abstract （770）   HTML （86）    PDF（pc） （2300KB）（8599）       Knowledge map    Save [Significance] The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios. [Progress] Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots. [Conclusions and Prospects] To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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State-of-the-art and Prospect of Research on Key Technical for Unmanned Farms of Field Corp YIN Yanxin, MENG Zhijun, ZHAO Chunjiang, WANG Hao, WEN Changkai, CHEN Jingping, LI Liwei, DU Jingwei, WANG Pei, AN Xiaofei, SHANG Yehua, ZHANG Anqi, YAN Bingxin, WU Guangwei Smart Agriculture    2022, 4 (4): 1-25.   DOI: 10.12133/j.smartag.SA202212005 Abstract （3594）   HTML （794）    PDF（pc） （2582KB）（7800）       Knowledge map    Save As one of the important way for constructing smart agriculture, unmanned farms are the most attractive in nowadays, and have been explored in many countries. Generally, data, knowledge and intelligent equipment are the core elements of unmanned farms. It deeply integrates modern information technologies such as the Internet of Things, big data, cloud computing, edge computing, and artificial intelligence with agriculture to realize agricultural production information perception, quantitative decision-making, intelligent control, precise input and personalized services. In the paper, the overall technical architecture of unmanned farms is introduced, and five kinds of key technologies of unmanned farms are proposed, which include information perception and intelligent decision-making technology, precision control technology and key equipment for agriculture, automatic driving technology in agriculture, unmanned operation agricultural equipment, management and remote controlling system for unmanned farms. Furthermore, the latest research progress of the above technologies both worldwide are analyzed. Based on which, critical scientific and technological issues to be solved for developing unmanned farms in China are proposed, include unstructured environment perception of farmland, automatic drive for agriculture machinery in complex and changeable farmland environment, autonomous task assignment and path planning of unmanned agricultural machinery, autonomous cooperative operation control of unmanned agricultural machinery group. Those technologies are challenging and absolutely, and would be the most competitive commanding height in the future. The maize unmanned farm constructed in the city of Gongzhuling, Jilin province, China, was also introduced in detail. The unmanned farms is mainly composed of information perception system, unmanned agricultural equipment, management and controlling system. The perception system obtains and provides the farmland information, maize growth, pest and disease information of the farm. The unmanned agricultural machineries could complete the whole process of the maize mechanization under unattended conditions. The management and controlling system includes the basic GIS, remote controlling subsystem, precision operation management subsystem and working display system for unmanned agricultural machineries. The application of the maize unmanned farm has improved maize production efficiency (the harvesting efficiency has been increased by 3－4 times) and reduced labors. Finally, the paper summarizes the important role of the unmanned farm technology were summarized in solving the problems such as reduction of labors, analyzes the opportunities and challenges of developing unmanned farms in China, and put forward the strategic goals and ideas of developing unmanned farm in China. Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Prospects of Key Navigation Technologies for Facility Agricultural Robots HE Yong, HUANG Zhenyu, YANG Ningyuan, LI Xiyao, WANG Yuwei, FENG Xuping Smart Agriculture    2024, 6 (5): 1-19.   DOI: 10.12133/j.smartag.SA202404006 Abstract （1548）   HTML （326）    PDF（pc） （2130KB）（7364）       Knowledge map    Save [Significance] With the rapid development of robotics technology and the persistently rise of labor costs, the application of robots in facility agriculture is becoming increasingly widespread. These robots can enhance operational efficiency, reduce labor costs, and minimize human errors. However, the complexity and diversity of facility environments, including varying crop layouts and lighting conditions, impose higher demands on robot navigation. Therefore, achieving stable, accurate, and rapid navigation for robots has become a key issue. Advanced sensor technologies and algorithms have been proposed to enhance robots' adaptability and decision-making capabilities in dynamic environments. This not only elevates the automation level of agricultural production but also contributes to more intelligent agricultural management. [Progress] This paper reviews the key technologies of automatic navigation for facility agricultural robots. It details beacon localization, inertial positioning, simultaneous localization and mapping (SLAM) techniques, and sensor fusion methods used in autonomous localization and mapping. Depending on the type of sensors employed, SLAM technology could be subdivided into vision-based, laser-based and fusion systems. Fusion localization is further categorized into data-level, feature-level, and decision-level based on the types and stages of the fused information. The application of SLAM technology and fusion localization in facility agriculture has been increasingly common. Global path planning plays a crucial role in enhancing the operational efficiency and safety of facility aricultural robots. This paper discusses global path planning, classifying it into point-to-point local path planning and global traversal path planning. Furthermore, based on the number of optimization objectives, it was divided into single-objective path planning and multi-objective path planning. In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios. [Conclusions and Prospects] Currently, the challenges for facility agricultural robot navigation include complex scenarios with significant occlusions, cost constraints, low operational efficiency and the lack of standardized platforms and public datasets. These issues not only affect the practical application effectiveness of robots but also constrain the further advancement of the industry. To address these challenges, future research can focus on developing multi-sensor fusion technologies, applying and optimizing advanced algorithms, investigating and implementing multi-robot collaborative operations and establishing standardized and shared data platforms. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Metaverse: Key Technologies, Application Scenarios, Challenges and Prospects CHEN Feng, SUN Chuanheng, XING Bin, LUO Na, LIU Haishen Smart Agriculture    2022, 4 (4): 126-137.   DOI: 10.12133/j.smartag.SA202206006 Abstract （2244）   HTML （299）    PDF（pc） （1045KB）（6583）       Knowledge map    Save As an emerging concept, metaverse has attracted extensive attention from industry, academia and scientific research field. The combination of agriculture and metaverse will greatly promote the development of agricultural informatization and agricultural intelligence, provide new impetus for the transformation and upgrading of agricultural intelligence. Firstly, to expound feasibility of the application research of metaverse in agriculture, the basic principle and key technologies of agriculture metaverse were briefly described, such as blockchain, non-fungible token, 5G/6G, artificial intelligence, Internet of Things, 3D reconstruction, cloud computing, edge computing, augmented reality, virtual reality, mixed reality, brain computer interface, digital twins and parallel system. Then, the main scenarios of three agricultural applications of metaverse in the fields of virtual farm, agricultural teaching system and agricultural product traceability system were discussed. Among them, virtual farm is one of the most important applications of agricultural metaverse. Agricultural metaverse can help the growth of crops and the raising of livestock and poultry in the field of agricultural production, provide a three-dimensional and visual virtual leisure agricultural experience, provide virtual characters in the field of agricultural product promotion. The agricultural metaverse teaching system can provide virtual agricultural teaching similar to natural scenes, save training time and improve training efficiency by means of fragmentation. Traceability of agricultural products can let consumers know the production information of agricultural products and feel more confident about enterprises and products. Finally, the challenges in the development of agricultural metaverse were summarized in the aspects of difficulties in establishing agricultural metaverse system, weak communication foundation of agricultural metaverse, immature agricultural metaverse hardware equipment and uncertain agricultural meta universe operation, and the future development directions of agricultural metaverse were prospected. In the future, researches on the application of metaverse, agricultural growth mechanism, and low power wireless communication technologies are suggested to be carried out. A rural broadband network covering households can be established. The industrialization application of agricultural meta universe can be promoted. This review can provide theoretical references and technical supports for the development of metaverse in the field of agriculture. Reference | Related Articles | Metrics | Comments（0）

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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming Smart Agriculture    2024, 6 (5): 20-39.   DOI: 10.12133/j.smartag.SA202405022 Abstract （1103）   HTML （179）    PDF（pc） （4030KB）（6452）       Knowledge map    Save [Significance] Fruit-picking robot stands as a crucial solution for achieving intelligent fruit harvesting. Significant progress has been made in developing foundational methods for picking robots, such as fruit recognition, orchard navigation, path planning for picking, and robotic arm control, the practical implementation of a seamless picking system that integrates sensing, movement, and picking capabilities still encounters substantial technical hurdles. In contrast to current picking systems, the next generation of fruit-picking robots aims to replicate the autonomous skills exhibited by human fruit pickers. This involves effectively performing ongoing tasks of perception, movement, and picking without human intervention. To tackle this challenge, this review delves into the latest research methodologies and real-world applications in this field, critically assesses the strengths and limitations of existing methods and categorizes the essential components of continuous operation into three sub-modules: local target recognition, global mapping, and operation planning. [Progress] Initially, the review explores methods for recognizing nearby fruit and obstacle targets. These methods encompass four main approaches: low-level feature fusion, high-level feature learning, RGB-D information fusion, and multi-view information fusion, respectively. Each of these approaches incorporates advanced algorithms and sensor technologies for cluttered orchard environments. For example, low-level feature fusion utilizes basic attributes such as color, shapes and texture to distinguish fruits from backgrounds, while high-level feature learning employs more complex models like convolutional neural networks to interpret the contextual relationships within the data. RGB-D information fusion brings depth perception into the mix, allowing robots to gauge the distance to each fruit accurately. Multi-view information fusion tackles the issue of occlusions by combining data from multiple cameras and sensors around the robot, providing a more comprehensive view of the environment and enabling more reliable sensing. Subsequently, the review shifts focus to orchard mapping and scene comprehension on a broader scale. It points out that current mapping methods, while effective, still struggle with dynamic changes in the orchard, such as variations of fruits and light conditions. Improved adaptation techniques, possibly through machine learning models that can learn and adjust to different environmental conditions, are suggested as a way forward. Building upon the foundation of local and global perception, the review investigates strategies for planning and controlling autonomous behaviors. This includes not only the latest advancements in devising movement paths for robot mobility but also adaptive strategies that allow robots to react to unexpected obstacles or changes within the whole environment. Enhanced strategies for effective fruit picking using the Eye-in-Hand system involve the development of more dexterous robotic hands and improved algorithms for precisely predicting the optimal picking point of each fruit. The review also identifies a crucial need for further advancements in the dynamic behavior and autonomy of these technologies, emphasizing the importance of continuous learning and adaptive control systems to improve operational efficiency in diverse orchard environments. [Conclusions and Prospects] The review underscores the critical importance of coordinating perception, movement, and picking modules to facilitate the transition from a basic functional prototype to a practical machine. Moreover, it emphasizes the necessity of enhancing the robustness and stability of core algorithms governing perception, planning, and control, while ensuring their seamless coordination which is a central challenge that emerges. Additionally, the review raises unresolved questions regarding the application of picking robots and outlines future trends, include deeper integration of stereo vision and deep learning, enhanced global vision sampling, and the establishment of standardized evaluation criteria for overall operational performance. The paper can provide references for the eventual development of robust, autonomous, and commercially viable picking robots in the future. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Big Models in Agriculture: Key Technologies, Application and Future Directions GUO Wang, YANG Yusen, WU Huarui, ZHU Huaji, MIAO Yisheng, GU Jingqiu Smart Agriculture    2024, 6 (2): 1-13.   DOI: 10.12133/j.smartag.SA202403015 Abstract （3723）   HTML （531）    PDF（pc） （1482KB）（6381）       Knowledge map    Save [Significance] Big Models, or Foundation Models, have offered a new paradigm in smart agriculture. These models, built on the Transformer architecture, incorporate numerous parameters and have undergone extensive training, often showing excellent performance and adaptability, making them effective in addressing agricultural issues where data is limited. Integrating big models in agriculture promises to pave the way for a more comprehensive form of agricultural intelligence, capable of processing diverse inputs, making informed decisions, and potentially overseeing entire farming systems autonomously. [Progress] The fundamental concepts and core technologies of big models are initially elaborated from five aspects: the generation and core principles of the Transformer architecture, scaling laws of extending big models, large-scale self-supervised learning, the general capabilities and adaptions of big models, and the emerging capabilities of big models. Subsequently, the possible application scenarios of the big model in the agricultural field are analyzed in detail, the development status of big models is described based on three types of the models: Large language models (LLMs), large vision models (LVMs), and large multi-modal models (LMMs). The progress of applying big models in agriculture is discussed, and the achievements are presented. [Conclusions and Prospects] The challenges and key tasks of applying big models technology in agriculture are analyzed. Firstly, the current datasets used for agricultural big models are somewhat limited, and the process of constructing these datasets can be both expensive and potentially problematic in terms of copyright issues. There is a call for creating more extensive, more openly accessible datasets to facilitate future advancements. Secondly, the complexity of big models, due to their extensive parameter counts, poses significant challenges in terms of training and deployment. However, there is optimism that future methodological improvements will streamline these processes by optimizing memory and computational efficiency, thereby enhancing the performance of big models in agriculture. Thirdly, these advanced models demonstrate strong proficiency in analyzing image and text data, suggesting potential future applications in integrating real-time data from IoT devices and the Internet to make informed decisions, manage multi-modal data, and potentially operate machinery within autonomous agricultural systems. Finally, the dissemination and implementation of these big models in the public agricultural sphere are deemed crucial. The public availability of these models is expected to refine their capabilities through user feedback and alleviate the workload on humans by providing sophisticated and accurate agricultural advice, which could revolutionize agricultural practices. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Crop Stress Sensing and Plant Phenotyping Systems: A Review BAI Geng, GE Yufeng Smart Agriculture    2023, 5 (1): 66-81.   DOI: 10.12133/j.smartag.SA202211001 Abstract （1545）   HTML （72）    PDF（pc） （1595KB）（6104）       Knowledge map    Save Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Pineapple Maturity Analysis in Natural Environment Based on MobileNet V3-YOLOv4 LI Yangde, MA Xiaohui, WANG Ji Smart Agriculture    2023, 5 (2): 35-44.   DOI: 10.12133/j.smartag.SA202211007 Abstract （473）   HTML （60）    PDF（pc） （1858KB）（5928）       Knowledge map    Save [Objective] Pineapple is a common tropical fruit, and its ripeness has an important impact on the storage and marketing. It is particularly important to analyze the maturity of pineapple fruit before picking. Deep learning technology can be an effective method to achieve automatic recognition of pineapple maturity. To improve the accuracy and rate of automatic recognition of pineapple maturity, a new network model named MobileNet V3-YOLOv4 was proposed in this study. [Methods] Firstly, pineapple maturity analysis data set was constructed. A total of 1580 images were obtained, with 1264 images selected as the training set, 158 images as the validation set, and 158 images as the test set. Pineapple photos were taken in natural environment. In order to ensure the diversity of the data set and improve the robustness and generalization of the network, pineapple photos were taken under the influence of different factors such as branches and leaves occlusion, uneven lighting, overlapping shadows, etc. and the location, weather and growing environment of the collection were different. Then, according to the maturity index of pineapple, the photos of pineapple with different maturity were marked, and the labels were divided into yellow ripeness and green ripeness. The annotated images were taken as data sets and input into the network for training. Aiming at the problems of the traditional YOLOv4 network, such as large number of parameters, complex network structure and slow reasoning speed, a more optimized lightweight MobileNet V3-YOLOv4 network model was proposed. The model utilizes the benck structure to replace the Resblock in the CSPDarknet backbone network of YOLOv4. Meanwhile, in order to verify the effectiveness of the MobileNet V3-YOLOv4 network, MobileNet V1-YOLOv4 model and MobileNet V2-YOLOv4 model were also trained. Five different single-stage and two-stage network models, including R-CNN, YOLOv3, SSD300, Retinanet and Centernet were compared with each evaluation index to analyze the performance superiority of MobileNet V3-YOLOv4 model. Results and Discussions] MobileNet V3-YOLOv4 was validated for its effectiveness in pineapple maturity detection through experiments comparing model performance, model classification prediction, and accuracy tests in complex pineapple detection environments.The experimental results show that, in terms of model performance comparison, the training time of MobileNet V3-YOLOv4 was 11,924 s, with an average training time of 39.75 s per round, the number of parameters was 53.7 MB, resulting in a 25.59% reduction in the saturation time compared to YOLOv4, and the parameter count accounted for only 22%. The mean average precision (mAP) of the trained MobileNet V3-YOLOv4 in the verification set was 53.7 MB. In order to validate the classification prediction performance of the MobileNet V3-YOLOv4 model, four metrics, including Recall score, F1 Score, Precision, and average precision (AP), were utilized to classify and recognize pineapples of different maturities. The experimental results demonstrate that MobileNet V3-YOLOv4 exhibited significantly higher Precision, AP, and F1 Score the other. For the semi-ripe stage, there was a 4.49% increase in AP, 0.07 improvement in F1 Score, 1% increase in Recall, and 3.34% increase in Precision than YOLOv4. As for the ripe stage, there was a 6.06% increase in AP, 0.13 improvement in F1 Score, 16.55% increase in Recall, and 6.25% increase in Precision. Due to the distinct color features of ripe pineapples and their easy differentiation from the background, the improved network achieved a precision rate of 100.00%. Additionally, the mAP and reasoning speed (Frames Per Second, FPS) of nine algorithms were examined. The results showed that MobileNet V3-YOLOv4 achieved an mAP of 90.92%, which was 5.28% higher than YOLOv4 and 3.67% higher than YOLOv3. The FPS was measured at 80.85 img/s, which was 40.28 img/s higher than YOLOv4 and 8.91 img/s higher than SSD300. The detection results of MobileNet V3-YOLOv4 for pineapples of different maturities in complex environments indicated a 100% success rate for both the semi-ripe and ripe stages, while YOLOv4, MobileNet V1-YOLOv4, and MobileNet V2-YOLOv4 exhibited varying degrees of missed detections. [Conclusions] Based on the above experimental results, it can be concluded that MobileNet V3-YOLOv4 proposed in this study could not only reduce the training speed and parameter number number, but also improve the accuracy and reasoning speed of pineapple maturity recognition, so it has important application prospects in the field of smart orchard. At the same time, the pineapple photo data set collected in this research can also provide valuable data resources for the research and application of related fields. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example ZUO Min, HU Tianyu, DONG Wei, ZHANG Kexin, ZHANG Qingchuan Smart Agriculture    2023, 5 (1): 34-43.   DOI: 10.12133/j.smartag.SA202302001 Abstract （958）   HTML （199）    PDF（pc） （1323KB）（5916）       Knowledge map    Save Ensuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability. However, the forecasting of agricultural products logistics demand is actually complicated, and it will be affected by various factors in the forecasting process. Therefore, in order to ensure the accuracy of forecasting the logistics demand of agricultural products, many influencing factors need to be considered. In this study, the logistics demand of agricultural products is taken as the research object, relevant indicators from 2017 to 2021 were selected as characteristic independent variables and a neural network model for forecasting the logistics demand of agricultural products was constructed by using Informer neural network. Taking Henan province, Hubei province and Hunan province in Central China as examples, the logistics demands of agricultural products in the three provinces were predicted. At the same time, long short-term memory network (LSTM) and Transformer neural network were used to forecast the demand of agricultural products logistics in three provinces of Central China, and the prediction results of the three models were compared. The results showed that the average percentage of prediction test error based on Informer neural network model constructed in this study was 3.39%, which was lower than that of LSTM and Transformer neural network models of 4.43% and 4.35%. The predicted value of Informer neural network model for three provinces was close to the actual value. The predicted value of Henan province in 2021 was 4185.33, the actual value was 4048.10, and the error was 3.389%. The predicted value of Hubei province in 2021 was 2503.64, the actual value was 2421.78, and the error was 3.380%. The predicted value of Hunan province in 2021 was 2933.31, the actual value was 2836.86, and the error was 3.340%. Therefore, it showed that the model can accurately predict the demand of agricultural products logistics in three provinces of Central China, and can provide a basis for rational planning and policy making of agricultural products logistics. Finally, the model and parameters were used to predict the logistics demand of agricultural products in Henan, Hunan, and Hubei provinces in 2023, and the predicted value of Henan province in 2023 was 4217.13; Hubei province was 2521.47, and Hunan province was 2974.65, respectively. The predicted values for the three provinces in 2023 are higher than the predicted values in 2021. Therefore, based on the logistics and transportation supporting facilities in 2021, it is necessary to ensure logistics and transportation efficiency and strengthen logistics and transportation capacity, so as to meet the growing logistics demand in Central China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Technology Knowledge Intelligent Question-Answering System Based on Large Language Model WANG Ting, WANG Na, CUI Yunpeng, LIU Juan Smart Agriculture    2023, 5 (4): 105-116.   DOI: 10.12133/j.smartag.SA202311005 Abstract （2822）   HTML （391）    PDF（pc） （1475KB）（5811）       Knowledge map    Save [Objective] The rural revitalization strategy presents novel requisites for the extension of agricultural technology. However, the conventional method encounters the issue of a contradiction between supply and demand. Therefore, there is a need for further innovation in the supply form of agricultural knowledge. Recent advancements in artificial intelligence technologies, such as deep learning and large-scale neural networks, particularly the advent of large language models (LLMs), render anthropomorphic and intelligent agricultural technology extension feasible. With the agricultural technology knowledge service of fruit and vegetable as the demand orientation, the intelligent agricultural technology question answering system was built in this research based on LLM, providing agricultural technology extension services, including guidance on new agricultural knowledge and question-and-answer sessions. This facilitates farmers in accessing high-quality agricultural knowledge at their convenience. [Methods] Through an analysis of the demands of strawberry farmers, the agricultural technology knowledge related to strawberry cultivation was categorized into six themes: basic production knowledge, variety screening, interplanting knowledge, pest diagnosis and control, disease diagnosis and control, and drug damage diagnosis and control. Considering the current situation of agricultural technology, two primary tasks were formulated: named entity recognition and question answering related to agricultural knowledge. A training corpus comprising entity type annotations and question-answer pairs was constructed using a combination of automatic machine annotation and manual annotation, ensuring a small yet high-quality sample. After comparing four existing Large Language Models (Baichuan2-13B-Chat, ChatGLM2-6B, Llama 2-13B-Chat, and ChatGPT), the model exhibiting the best performance was chosen as the base LLM to develop the intelligent question-answering system for agricultural technology knowledge. Utilizing a high-quality corpus, pre-training of a Large Language Model and the fine-tuning method, a deep neural network with semantic analysis, context association, and content generation capabilities was trained. This model served as a Large Language Model for named entity recognition and question answering of agricultural knowledge, adaptable to various downstream tasks. For the task of named entity recognition, the fine-tuning method of Lora was employed, fine-tuning only essential parameters to expedite model training and enhance performance. Regarding the question-answering task, the Prompt-tuning method was used to fine-tune the Large Language Model, where adjustments were made based on the generated content of the model, achieving iterative optimization. Model performance optimization was conducted from two perspectives: data and model design. In terms of data, redundant or unclear data was manually removed from the labeled corpus. In terms of the model, a strategy based on retrieval enhancement generation technology was employed to deepen the understanding of agricultural knowledge in the Large Language Model and maintain real-time synchronization of knowledge, alleviating the problem of LLM hallucination. Drawing upon the constructed Large Language Model, an intelligent question-answering system was developed for agricultural technology knowledge. This system demonstrates the capability to generate high-precision and unambiguous answers, while also supporting the functionalities of multi-round question answering and retrieval of information sources. [Results and Discussions] Accuracy rate and recall rate served as indicators to evaluate the named entity recognition task performance of the Large Language Models. The results indicated that the performance of Large Language Models was closely related to factors such as model structure, the scale of the labeled corpus, and the number of entity types. After fine-tuning, the ChatGLM Large Language Model demonstrated the highest accuracy and recall rate. With the same number of entity types, a higher number of annotated corpora resulted in a higher accuracy rate. Fine-tuning had different effects on different models, and overall, it improved the average accuracy of all models under different knowledge topics, with ChatGLM, Llama, and Baichuan values all surpassing 85%. The average recall rate saw limited increase, and in some cases, it was even lower than the values before fine-tuning. Assessing the question-answering task of Large Language Models using hallucination rate and semantic similarity as indicators, data optimization and retrieval enhancement generation techniques effectively reduced the hallucination rate by 10% to 40% and improved semantic similarity by more than 15%. These optimizations significantly enhanced the generated content of the models in terms of correctness, logic, and comprehensiveness. [Conclusion] The pre-trained Large Language Model of ChatGLM exhibited superior performance in named entity recognition and question answering tasks in the agricultural field. Fine-tuning pre-trained Large Language Models for downstream tasks and optimizing based on retrieval enhancement generation technology mitigated the problem of language hallucination, markedly improving model performance. Large Language Model technology has the potential to innovate agricultural technology knowledge service modes and optimize agricultural knowledge extension. This can effectively reduce the time cost for farmers to obtain high-quality and effective knowledge, guiding more farmers towards agricultural technology innovation and transformation. However, due to challenges such as unstable performance, further research is needed to explore optimization methods for Large Language Models and their application in specific scenarios. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Status Quo of Waterfowl Intelligent Farming Research Review and Development Trend Analysis LIU Youfu, XIAO Deqin, ZHOU Jiaxin, BIAN Zhiyi, ZHAO Shengqiu, HUANG Yigui, WANG Wence Smart Agriculture    2023, 5 (1): 99-110.   DOI: 10.12133/j.smartag.SA202205007 Abstract （969）   HTML （68）    PDF（pc） （2057KB）（5569）       Knowledge map    Save Waterfowl farming in China is developing rapidly in the direction of large-scale, standardization and intelligence. The research and application of intelligent farming equipment and information technology is the key to promote the healthy and sustainable development of waterfowl farming, which is important to improve the output efficiency of waterfowl farming, reduce the reliance on labor in the production process, fit the development concept of green and environmental protection and achieve high-quality transformational development. In this paper, the latest research and inventions of intelligent waterfowl equipment, waterfowl shed environment intelligent control technology and intelligent waterfowl feeding, drinking water, dosing and disinfection and automatic manure treatment equipment were introduced. At present, compared to pigs, chickens and cattle, the intelligent equipment of waterfowl are still relatively backward. Most waterfowl houses are equipped with chicken equipment directly, lacking improvements for waterfowl. Moreover, the linkage between the equipment is poor and not integrated with the breeding mode and shed structure of waterfowl, resulting in low utilization. Therefore, there is a need to develop and improve equipment for the physiological growth characteristics of waterfowl from the perspective of their breeding welfare. In addition, the latest research advances in the application of real-time production information collection and intelligent management technologies were present. The information collection technologies included visual imaging technology, sound capture systems, and wearable sensors were present. Since the researches of ducks and geese is few, the research of poultry field, which can provide a reference for the waterfowl were also summarized. The research of information perception and processing of waterfowl is currently in its initial stage. Information collection techniques need to be further tailored to the physiological growth characteristics of waterfowl, and better deep learning models need to be established. The waterfowl management platform, taking the intelligent management platform developed by South China Agricultural University as an example were also described. Finally, the intelligent application of the waterfowl industry was pointed out, and the future trends of intelligent farming with the development of mechanized and intelligent equipment for waterfowl in China to improve the recommendations were analyzed. The current waterfowl farming is in urgent need of intelligent equipment reform and upgrading of the industry for support. In the future, intelligent equipment for waterfowl, information perception methods and control platforms are in urgent to be developed. When upgrading the industry, it is necessary to develop a development strategy that fits the current waterfowl farming model in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Apple Phenological Period Identification in Natural Environment Based on Improved ResNet50 Model LIU Yongbo, GAO Wenbo, HE Peng, TANG Jiangyun, HU Liang Smart Agriculture    2023, 5 (2): 13-22.   DOI: 10.12133/j.smartag.SA202304009 Abstract （722）   HTML （125）    PDF（pc） （2822KB）（5514）       Knowledge map    Save [Objective] Aiming at the problems of low accuracy and incomplete coverage of image recognition of phenological period of apple in natural environment by traditional methods, an improved ResNet50 model was proposed for phenological period recognition of apple. [Methods] With 8 kinds of phenological period images of Red Fuji apple in Sichuan plateau area as the research objects and 3 sets of spherical cameras built in apple orchard as acquisition equipment, the original data set of 9800 images of apple phenological period were obtained, labeled by fruit tree experts. Due to the different duration of each phenological period of apple, there were certain differences in the quantity of collection. In order to avoid the problem of decreasing model accuracy due to the quantity imbalance, data set was enhanced by random cropping, random rotation, horizontal flip and brightness adjustment, and the original data set was expanded to 32,000 images. It was divided into training set (25,600 images), verification set (3200 images) and test set (3200 images) in a ratio of 8:1:1. Based on the ResNet50 model, the SE (Squeeze and Excitation Network) channel attention mechanism and Adam optimizer were integrated. SE channel attention was introduced at the end of each residual module in the benchmark model to improve the model's feature extraction ability for plateau apple tree images. In order to achieve fast convergence of the model, the Adam optimizer was combined with the cosine annealing attenuation learning rate, and ImageNet was selected as the pre-training model to realize intelligent recognition of plateau Red Fuji apple phenological period under natural environment. A "Intelligent Monitoring and Production Management Platform for Fruit Tree Growth Period" has been developed using the identification model of apple tree phenology. In order to reduce the probability of model misjudgment, improve the accuracy of model recognition, and ensure precise control of the platform over the apple orchard, three sets of cameras deployed in the apple orchard were set to capture motion trajectories, and images were collected at three time a day: early, middle, and late, a total of 27 images per day were collected. The model calculated the recognition results of 27 images and takes the category with the highest number of recognition as the output result to correct the recognition rate and improve the reliability of the platform. [Results and Discussions] Experiments were carried out on 32,000 apple tree images. The results showed that when the initial learning rate of Adam optimizer was set as 0.0001, the accuracy of the test model tended to the optimal, and the loss value curve converged the fastest. When the initial learning rate was set to 0.0001 and the iteration rounds are set to 30, 50 and 70, the accuracies of the optimal verification set obtained by the model was 0.9354, 0.9635 and 0.9528, respectively. Therefore, the improved ResNet50 model selects the learning rate of 0.0001 and iteration rounds of 50 as the training parameters of the Adam optimizer. Ablation experiments showed that the accuracy of validation set and test set were increased by 0.8% and 2.99% in the ResNet50 model with increased SE attention mechanism, respectively. The validation set accuracy and test set accuracy of the ResNet50 model increased by 2.19% and 1.42%, respectively, when Adam optimizer was added. The accuracy of validation set and test set was 2.33% and 3.65%, respectively. The accuracy of validation set was 96.35%, the accuracy of test set was 91.94%, and the average detection time was 2.19 ms.Compared with the AlexNet, VGG16, ResNet18, ResNet34, and ResNet101 models, the improved ResNet50 model improved the accuracy of the optimal validation set by 9.63%, 5.07%, 5.81%, 4.55%, and 0.96%, respectively. The accuracy of the test set increased by 12.31%, 6.88%, 8.53%, 8.67%, and 5.58%, respectively. The confusion matrix experiment result showed that the overall recognition rate of the improved ResNet50 model for the phenological period of apple tree images was more than 90%, of which the accuracy rate of bud stage and dormancy stage was the lowest, and the probability of mutual misjudgment was high, and the test accuracy rates were 89.50% and 87.44% respectively. There were also a few misjudgments during the young fruit stage, fruit enlargement stage, and fruit coloring stage due to the similarity in characteristics between adjacent stages. The external characteristics of the Red Fuji apple tree were more obvious during the flowering and fruit ripening stages, and the model had the highest recognition rate for the flowering and fruit ripening stages, with test accuracy reaching 97.50% and 97.49%, respectively. [Conclusions] The improved ResNet50 can effectively identify apple phenology, and the research results can provide reference for the identification of orchard phenological period. After integration into the intelligent monitoring production management platform of fruit tree growth period, intelligent management and control of apple orchard can be realized. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Intelligent Knowledge Service: Overview and Future Perspectives ZHAO Ruixue, YANG Chenxue, ZHENG Jianhua, LI Jiao, WANG Jian Smart Agriculture    2022, 4 (4): 105-125.   DOI: 10.12133/j.smartag.SA202207009 Abstract （1980）   HTML （211）    PDF（pc） （1435KB）（5449）       Knowledge map    Save The wide application of advanced information technologies such as big data, Internet of Things and artificial intelligence in agriculture has promoted the modernization of agriculture in rural areas and the development of smart agriculture. This trend has also led to the boost of demands for technology and knowledge from a large amount of agricultural business entities. Faced with problems such as dispersiveness of knowledges, hysteric knowledge update, inadequate agricultural information service and prominent contradiction between supply and demand of knowledge, the agricultural knowledge service has become an important engine for the transformation, upgrading and high-quality development of agriculture. To better facilitate the agriculture modernization in China, the research and application perspectives of agricultural knowledge services were summarized and analyzed. According to the whole life cycle of agricultural data, based on the whole agricultural industry chain, a systematic framework for the construction of agricultural intelligent knowledge service systems towards the requirement of agricultural business entities was proposed. Three layers of techniques in necessity were designed, ranging from AIoT-based agricultural situation perception to big data aggregation and governance, and from agricultural knowledge organization to computation/mining based on knowledge graph and then to multi-scenario-based agricultural intelligent knowledge service. A wide range of key technologies with comprehensive discussion on their applications in agricultural intelligent knowledge service were summarized, including the aerial and ground integrated Artificial Intelligence & Internet-of-Things (AIoT) full-dimensional of agricultural condition perception, multi-source heterogeneous agricultural big data aggregation/governance, knowledge modeling, knowledge extraction, knowledge fusion, knowledge reasoning, cross-media retrieval, intelligent question answering, personalized recommendation, decision support. At the end, the future development trends and countermeasures were discussed, from the aspects of agricultural data acquisition, model construction, knowledge organization, intelligent knowledge service technology and application promotion. It can be concluded that the agricultural intelligent knowledge service is the key to resolve the contradiction between supply and demand of agricultural knowledge service, can provide support in the realization of the advance from agricultural cross-media data analytics to knowledge reasoning, and promote the upgrade of agricultural knowledge service to be more personalized, more precise and more intelligent. Agricultural knowledge service is also an important support for agricultural science and technologies to be more self-reliance, modernized, and facilitates substantial development and upgrading of them in a more effective manner. Reference | Related Articles | Metrics | Comments（0）

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Wheat Lodging Area Recognition Method Based on Different Resolution UAV Multispectral Remote Sensing Images WEI Yongkang, YANG Tiancong, DING Xinyao, GAO Yuezhi, YUAN Xinru, HE Li, WANG Yonghua, DUAN Jianzhao, FENG Wei Smart Agriculture    2023, 5 (2): 56-67.   DOI: 10.12133/j.smartag.SA202304014 Abstract （636）   HTML （78）    PDF（pc） （4042KB）（5326）       Knowledge map    Save [Objective] To quickly and accurately assess the situation of crop lodging disasters, it is necessary to promptly obtain information such as the location and area of the lodging occurrences. Currently, there are no corresponding technical standards for identifying crop lodging based on UAV remote sensing, which is not conducive to standardizing the process of obtaining UAV data and proposing solutions to problems. This study aims to explore the impact of different spatial resolution remote sensing images and feature optimization methods on the accuracy of identifying wheat lodging areas. [Methods] Digital orthophoto images (DOM) and digital surface models (DSM) were collected by UAVs with high-resolution sensors at different flight altitudes after wheat lodging. The spatial resolutions of these image data were 1.05, 2.09, and 3.26 cm. A full feature set was constructed by extracting 5 spectral features, 2 height features, 5 vegetation indices, and 40 texture features from the pre-processed data. Then three feature selection methods, ReliefF algorithm, RF-RFE algorithm, and Boruta-Shap algorithm, were used to construct an optimized subset of features at different flight altitudes to select the best feature selection method. The ReliefF algorithm retains features with weights greater than 0.2 by setting a threshold of 0.2; the RF-RFE algorithm quantitatively evaluated the importance of each feature and introduces variables in descending order of importance to determine classification accuracy; the Boruta-Shap algorithm performed feature subset screening on the full feature set and labels a feature as green when its importance score was higher than that of the shaded feature, defining it as an important variable for model construction. Based on the above-mentioned feature subset, an object-oriented classification model on remote sensing images was conducted using eCognition9.0 software. Firstly, after several experiments, the feature parameters for multi-scale segmentation in the object-oriented classification were determined, namely a segmentation scale of 1, a shape factor of 0.1, and a tightness of 0.5. Three object-oriented supervised classification algorithms, support vector machine (SVM), random forest (RF), and K nearest neighbor (KNN), were selected to construct wheat lodging classification models. The Overall classification accuracy and Kappa coefficient were used to evaluate the accuracy of wheat lodging identification. By constructing a wheat lodging classification model, the appropriate classification strategy was clarified and a technical path for lodging classification was established. This technical path can be used for wheat lodging monitoring, providing a scientific basis for agricultural production and improving agricultural production efficiency. [Results and Discussions] The results showed that increasing the altitude of the UAV to 90 m significantly improved flight efficiency of wheat lodging areas. In comparison to flying at 30 m for the same monitoring range, data acquisition time was reduced to approximately 1/6th, and the number of photos needed decreased from 62 to 6. In terms of classification accuracy, the overall classification effect of SVM is better than that of RF and KNN. Additionally, when the image spatial resolution varied from 1.05 to 3.26 cm, the full feature set and all three optimized feature subsets had the highest classification accuracy at a resolution of 1.05 cm, which was better than at resolutions of 2.09 and 3.26 cm. As the image spatial resolution decreased, the overall classification effect gradually deteriorated and the positioning accuracy decreased, resulting in poor spatial consistency of the classification results. Further research has found that the Boruta-Shap feature selection method can reduce data dimensionality and improve computational speed while maintaining high classification accuracy. Among the three tested spatial resolution conditions (1.05, 2.09, and 3.26 cm), the combination of SVM and Boruta-Shap algorithms demonstrated the highest overall classification accuracy. Specifically, the accuracy rates were 95.6%, 94.6%, and 93.9% for the respective spatial resolutions. These results highlighted the superior performance of this combination in accurately classifying the data and adapt to changes in spatial resolution. When the image resolution was 3.26 cm, the overall classification accuracy decreased by 1.81% and 0.75% compared to 1.05 and 2.09 cm; when the image resolution was 2.09 cm, the overall classification accuracy decreased by 1.06% compared to 1.05 cm, showing a relatively small difference in classification accuracy under different flight altitudes. The overall classification accuracy at an altitude of 90 m reached 95.6%, with Kappa coefficient of 0.914, meeting the requirements for classification accuracy. [Conclusions] The study shows that the object-oriented SVM classifier and the Boruta-Shap feature optimization algorithm have strong application extension advantages in identifying lodging areas in remote sensing images at multiple flight altitudes. These methods can achieve high-precision crop lodging area identification and reduce the influence of image spatial resolution on model stability. This helps to increase flight altitude, expand the monitoring range, improve UAV operation efficiency, and reduce flight costs. In practical applications, it is possible to strike a balance between classification accuracy and efficiency based on specific requirements and the actual scenario, thus providing guidance and support for the development of strategies for acquiring crop lodging information and evaluating wheat disasters. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Goals, Key Technologies, and Regional Models of Smart Farming for Field Crops in China LI Li, LI Minzan, LIU Gang, ZHANG Man, WANG Maohua Smart Agriculture    2022, 4 (4): 26-34.   DOI: 10.12133/j.smartag.SA202207003 Abstract （2194）   HTML （289）    PDF（pc） （853KB）（5140）       Knowledge map    Save Smart farming for field crops is a significant part of the smart agriculture. It aims at crop production, integrating modern sensing technology, new generation mobile communication technology, computer and network technology, Internet of Things(IoT), big data, cloud computing, blockchain and expert wisdom and knowledge. Deeply integrated application of biotechnology, engineering technology, information technology and management technology, it realizes accurate perception, quantitative decision-making, intelligent operation and intelligent service in the process of crop production, to significantly improve land output, resource utilization and labor productivity, comprehensively improves the quality, and promotes efficiency of agricultural products. In order to promote the sustainable development of the smart farming, through the analysis of the development process of smart agriculture, the overall objectives and key tasks of the development strategy were clarified, the key technologies in smart farming were condensed. Analysis and breakthrough of smart farming key technologies were crucial to the industrial development strategy. The main problems of the smart farming for field crops include: the lack of in-situ accurate measurement technology and special agricultural sensors, the large difference between crop model and actual production, the instantaneity, reliability, universality, and stability of the information transmission technologies, and the combination of intelligent agricultural equipment with agronomy. Based on the above analysis, five primary technologies and eighteen corresponding secondary technologies of smart farming for field crops were proposed, including: sensing technologies of environmental and biological information in field, agricultural IoT technologies and mobile internet, cloud computing and cloud service technologies in agriculture, big data analysis and decision-making technology in agriculture, and intelligent agricultural machinery and agricultural robots in fireld production. According to the characteristics of China's cropping region, the corresponding smart farming development strategies were proposed: large-scale smart production development zone in the Northeast region and Inner Mongolia region, smart urban agriculture and water-saving agriculture development zone in the region of Beijing, Tianjin, Hebei and Shandong, large-scale smart farming of cotton and smart dry farming green development comprehensive test zone in the Northwest arid region, smart farming of rice comprehensive development test zone in the Southeast coast region, and characteristic smart farming development zone in the Southwest mountain region. Finally, the suggestions were given from the perspective of infrastructure, key technology, talent and policy. Reference | Related Articles | Metrics | Comments（0）

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Real-Time Monitoring System for Rabbit House Environment Based on NB-IoT Network QIN Yingdong, JIA Wenshen Smart Agriculture    2023, 5 (1): 155-165.   DOI: 10.12133/j.smartag.SA202211008 Abstract （944）   HTML （97）    PDF（pc） （1662KB）（5053）       Knowledge map    Save To meet the needs of environmental monitoring and regulation in rabbit houses, a real-time environmental monitoring system for rabbit houses was proposed based on narrow band Internet of Things (NB-IoT). The system overcomes the limitations of traditional wired networks, reduces network costs, circuit components, and expenses is low. An Arduino development board and the Quectel BC260Y-NB-IoT network module were used, along with the message queuing telemetry transport (MQTT) protocol for remote telemetry transmission, which enables network connectivity and communication with an IoT cloud platform. Multiple sensors, including SGP30, MQ137, and 5516 photoresistors, were integrated into the system to achieve real-time monitoring of various environmental parameters within the rabbit house, such as sound decibels, light intensity, humidity, temperature, and gas concentrations. The collected data was stored for further analysis and could be used to inform environmental regulation and monitoring in rabbit houses, both locally and in the cloud. Signal alerts based on circuit principles were triggered when thresholds were exceeded, creating an optimal living environment for the rabbits. The advantages of NB-IoT networks and other networks, such as Wi-Fi and LoRa were compared. The technology and process of building a system based on the three-layer architecture of the Internet of Things was introduced. The prices of circuit components were analyzed, and the total cost of the entire system was less than 400 RMB. The system underwent network and energy consumption tests, and the transmission stability, reliability, and energy consumption were reasonable and consistent across different time periods, locations, and network connection methods. An average of 0.57 transactions per second (TPS) was processed by the NB-IoT network using the MQTT communication protocol, and 34.2 messages per minute were sent and received with a fluctuation of 1 message. The monitored device was found to have an average voltage of approximately 12.5 V, a current of approximately 0.42 A, and an average power of 5.3 W after continuous monitoring using an electricity meter. No additional power consumption was observed during communication. The performance of various sensors was tested through a 24-hour indoor test, during which temperature and lighting conditions showed different variations corresponding to day and night cycles. The readings were stably and accurately captured by the environmental sensors, demonstrating their suitability for long-term monitoring purposes. This system is can provide equipment cost and network selection reference values for remote or large-scale livestock monitoring devices. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Robots: Technology Progress, Challenges and Trends ZHAO Chunjiang, FAN Beibei, LI Jin, FENG Qingchun Smart Agriculture    2023, 5 (4): 1-15.   DOI: 10.12133/j.smartag.SA202312030 Abstract （3551）   HTML （472）    PDF（pc） （2498KB）（4784）       Knowledge map    Save [Significance] Autonomous and intelligent agricultural machinery, characterized by green intelligence, energy efficiency, and reduced emissions, as well as high intelligence and man-machine collaboration, will serve as the driving force behind global agricultural technology advancements and the transformation of production methods in the context of smart agriculture development. Agricultural robots, which utilize intelligent control and information technology, have the unique advantage of replacing manual labor. They occupy the strategic commanding heights and competitive focus of global agricultural equipment and are also one of the key development directions for accelerating the construction of China's agricultural power. World agricultural powers and China have incorporated the research, development, manufacturing, and promotion of agricultural robots into their national strategies, respectively strengthening the agricultural robot policy and planning layout based on their own agricultural development characteristics, thus driving the agricultural robot industry into a stable growth period. [Progress] This paper firstly delves into the concept and defining features of agricultural robots, alongside an exploration of the global agricultural robot development policy and strategic planning blueprint. Furthermore, sheds light on the growth and development of the global agricultural robotics industry; Then proceeds to analyze the industrial backdrop, cutting-edge advancements, developmental challenges, and crucial technology aspects of three representative agricultural robots, including farmland robots, orchard picking robots, and indoor vegetable production robots. Finally, summarizes the disparity between Chinese agricultural robots and their foreign counterparts in terms of advanced technologies. (1) An agricultural robot is a multi-degree-of-freedom autonomous operating equipment that possesses accurate perception, autonomous decision-making, intelligent control, and automatic execution capabilities specifically designed for agricultural environments. When combined with artificial intelligence, big data, cloud computing, and the Internet of Things, agricultural robots form an agricultural robot application system. This system has relatively mature applications in key processes such as field planting, fertilization, pest control, yield estimation, inspection, harvesting, grafting, pruning, inspection, harvesting, transportation, and livestock and poultry breeding feeding, inspection, disinfection, and milking. Globally, agricultural robots, represented by plant protection robots, have entered the industrial application phase and are gradually realizing commercialization with vast market potential. (2) Compared to traditional agricultural machinery and equipment, agricultural robots possess advantages in performing hazardous tasks, executing batch repetitive work, managing complex field operations, and livestock breeding. In contrast to industrial robots, agricultural robots face technical challenges in three aspects. Firstly, the complexity and unstructured nature of the operating environment. Secondly, the flexibility, mobility, and commoditization of the operation object. Thirdly, the high level of technology and investment required. (3) Given the increasing demand for unmanned and less manned operations in farmland production, China's agricultural robot research, development, and application have started late and progressed slowly. The existing agricultural operation equipment still has a significant gap from achieving precision operation, digital perception, intelligent management, and intelligent decision-making. The comprehensive performance of domestic products lags behind foreign advanced counterparts, indicating that there is still a long way to go for industrial development and application. Firstly, the current agricultural robots predominantly utilize single actuators and operate as single machines, with the development of multi-arm cooperative robots just emerging. Most of these robots primarily engage in rigid operations, exhibiting limited flexibility, adaptability, and functionality. Secondly, the perception of multi-source environments in agricultural settings, as well as the autonomous operation of agricultural robot equipment, relies heavily on human input. Thirdly, the progress of new teaching methods and technologies for human-computer natural interaction is rather slow. Lastly, the development of operational infrastructure is insufficient, resulting in a relatively low degree of "mechanization". [Conclusions and Prospects] The paper anticipates the opportunities that arise from the rapid growth of the agricultural robotics industry in response to the escalating global shortage of agricultural labor. It outlines the emerging trends in agricultural robot technology, including autonomous navigation, self-learning, real-time monitoring, and operation control. In the future, the path planning and navigation information perception of agricultural robot autonomy are expected to become more refined. Furthermore, improvements in autonomous learning and cross-scenario operation performance will be achieved. The development of real-time operation monitoring of agricultural robots through digital twinning will also progress. Additionally, cloud-based management and control of agricultural robots for comprehensive operations will experience significant growth. Steady advancements will be made in the innovation and integration of agricultural machinery and techniques. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Lightweight Intelligent Recognition of Saposhnikovia Divaricata (Turcz.) Schischk Originality Based on Improved ShuffleNet V2 ZHAO Yu, REN Yiping, PIAO Xinru, ZHENG Danyang, LI Dongming Smart Agriculture    2023, 5 (2): 104-114.   DOI: 10.12133/j.smartag.SA202304003 Abstract （468）   HTML （34）    PDF（pc） （1531KB）（4318）       Knowledge map    Save [Objective] Saposhnikovia divaricata (Turcz.) Schischk is a kind of traditional Chinese medicine. Currently, the methods of identifying the origin and quality of Saposhnikovia divaricata (Turcz.) Schischk are mainly based on their physical or chemical characteristics, which is impossible to make an accurate measurement of Groundness identification. With the continuous development of deep learning, its advantages of no manual extraction and high classification accuracy are widely used in different fields, and an attention-embedded ShuffleNet V2-based model was proposed in this study to address the problems of large computation and low accuracy of most convolutional neural network models in the identification of Chinese herbal medicine Saposhnikovia divaricata (Turcz.) Schischk. [Methods] The model architecture was adjusted to reduce the number of model parameters and computation without degrading the network performance, and the traditional residual network was replaced by the Hourglass residual network, while the SE attention mechanism was introduced to embed the hourglass residual network with additional channel attention into ShuffleNet V2. The important features were enhanced and the unimportant features were weakened by controlling the size of the channel ratio to make the extracted features more directional by SE attention. The SiLU activation function was used to replace the ReLU activation function to enhance the generalization ability of the model Enriching local feature learning. Therefore, a lightweight Shuffle-Hourglass SE model was proposed. The samples of Saposhnikovia divaricata (Turcz.) Schischk used in this research were samples from the main production areas, including more than 1000 samples from five production areas in Heilongjiang, Jilin, Hebei, Gansu and Inner Mongolia. A total of 5234 images of Saposhnikovia divaricata (Turcz.) Schischk were obtained by using cell phone photography indoors under white daylight, fully taking into account the geographical distribution differences of different Saposhnikovia divaricata (Turcz.) Schischk. The data set of Saposhnikovia divaricata (Turcz.) Schischk images was expanded to 10,120 by using random flip, random crop, brightness and contrast enhancement processes. In order to verify the effectiveness of the model proposed, four classical network models, VGG16, MobileNet V2, ShuffleNet V2 and SqueezeNet V2, were selected for comparison experiments, ECA ( Efficient Channel Attention ) attention mechanism, CBAM ( Convolutional Block Attention Module ) attention mechanism and CA attention mechanism were chosen to compare with SE. All attention mechanisms were introduced into the same position in the ShuffleNet V2 model, and ReLU, H-swish and ELU activation functions were selected for contrast experiments under the condition in which other parameters unchanged. In order to explore the performance improvement of ShuffleNet V2 model by using the attention mechanism of SE module, Hourglass residual block and activation function, Shuffle-Hourglass SE model ablation experiment was carried out. Finally, loss, accuracy, precision, recall and F1 score in test set and training set were used as evaluation indexes of model performances. [Results and Discussions] The results showed that the Shuffle-Hourglass SE model proposed achieved the best performances. An accuracy of 95.32%, recall of 95.28%, and F1 score of 95.27% were obtained in the test set, which was 2.09%, 2.1 %, and 2.19 % higher than the ShuffleNet V2 model, respectively. The test duration and model size were 246.34 ms and 3.23 M, respectively, which were not only optimal among Traditional CNN such as VGG and Desnet,but had great advantages among lightweight networks such as MobileNet V2、SqueezeNet V2 and ShufffleNet V2. Compared with the classical convolutional network VGG, 7.41% of the accuracy was improved, 71.89% of the test duration was reduced, and 96.76% of the model size was reduced by the Shuffle-Hourglass SE model proposed in this study. Although the test duration of ShuffleNet V2 and MobileNet V2 were similar, the accuracy and speed of the Shuffle-Hourglass SE model improved, which proved its better performance. Compared with MobileNet V2, the test duration was reduced by 69.31 ms, the model size was reduced by 1.98 M, and the accuracy was increased by 10.5 %. In terms of classification accuracy, the improved network maintains higher recognition accuracy and better classification performance. [Conclusions] The model proposed in this research is able to identify the Saposhnikovia divaricata (Turcz.) Schischk originality well while maintaining high identification accuracy and consuming less storage space, which is helpful for realizing real-time identification of Saposhnikovia divaricata (Turcz.) Schischk originality in the future low performance terminals. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Reconstruction of U.S. Regional-Scale Soybean SIF Based on MODIS Data and BP Neural Network YAO Jianen, LIU Haiqiu, YANG Man, FENG Jinying, CHEN Xiu, ZHANG Peipei Smart Agriculture    2024, 6 (5): 40-50.   DOI: 10.12133/j.smartag.SA202309006 Abstract （377）   HTML （44）    PDF（pc） （1648KB）（4088）       Knowledge map    Save [Objective] Sunlight-induced chlorophyll fluorescence (SIF) data obtained from satellites suffer from issues such as low spatial and temporal resolution, and discrete footprint because of the limitations imposed by satellite orbits. To address these problems, obtaining higher resolution SIF data, most reconstruction studies are based on low-resolution satellite SIF. Moreover, the spatial resolution of most SIF reconstruction products is still not enough to be directly used for the study of crop photosynthetic rate at the regional scale. Although some SIF products boast elevated resolutions, but these derive not from the original satellite SIF data reconstruct but instead evolve from secondary reconstructions based on preexisting SIF reconstruction products. Satellite OCO-2 (The Orbiting Carbon Obsevatory-2) equipped with a high-resolution spectrometer, OCO-2 SIF has higher spatial resolution (1.29×2.25 km) compared to other original SIF products, making it suitable in advancing the realm of high-resolution SIF data reconstruction, particularly within the context of regional-scale crop studies. [Methods] This research primarily exploration SIF reconstruct at the regional scale, mainly focused on the partial soybean planting regions nestled within the United States. The selection of MODIS raw data hinged on a meticulous consideration of environmental conditions, the distinctive physiological attributes of soybeans, and an exhaustive evaluation of factors intricately linked to OCO-2 SIF within these soybean planting regions. The primary tasks of this research encompassed reconstructing high resolution soybean SIF while concurrently executing a rigorous assessment of the reconstructed SIF's quality. During the dataset construction process, amalgamated SIF data from multiple soybean planting regions traversed by the OCO-2 satellite's footprint to retain as many of the available original SIF samples as possible. This approach provided the subsequent SIF reconstruction model with a rich source of SIF data. SIF data obtained beneath the satellite's trajectory were matched with various MODIS datasets, including enhanced vegetation index (EVI), fraction of photosynthetically active radiation (FPAR), and land surface temperature (LST), resulting in the creation of a multisource remote sensing dataset ultimately used for model training. Because of the multisource remote sensing dataset encompassed the most relevant explanatory variables within each SIF footprint coverage area concerning soybean physiological structure and environmental conditions. Through the activation functions in the BP neural network, it enhanced the understanding of the complex nonlinear relationships between the original SIF data and these MODIS products. Leveraging these inherent nonlinear relationships, compared and analyzed the effects of different combinations of explanatory variables on SIF reconstruction, mainly analyzing the three indicators of goodness of fit R2, root mean square error RMSE, and mean absolute error MAE, and then selecting the best SIF reconstruction model, generate a regional scale, spatially continuous, and high temporal resolution (500 m, 8 d) soybean SIF reconstruction dataset (BPSIF). [Results and Discussions] The research findings confirmed the strong performance of the SIF reconstruction model in predicting soybean SIF. After simultaneously incorporating EVI, FPAR, and LST as explanatory variables to model, achieved a goodness of fit with an R2 value of 0.84, this statistical metric validated the model's capability in predicting SIF data, it also reflected that the reconstructed 8 d time resolution of SIF data's reliability of applying to small-scale agricultural crop photosynthesis research with 500 m×500 m spatial scale. Based on this optimal model, generated the reconstructed SIF product (BPSIF). The Pearson correlation coefficient between the original OCO-2 SIF data and MODIS GPP stood were at a modest 0.53. In stark contrast, the correlation coefficient between BPSIF and MODIS Gross Primary Productivity (GPP) rosed significantly to 0.80. The increased correlation suggests that BPSIF could more accurately reflect the dynamic changes in GPP during the soybean growing season, making it more reliable compared to the original SIF data. Selected soybean planting areas in the United States with relatively single crop cultivation as the research area, based on high spatial resolution (1.29 km×2.25 km) OCO-2 SIF data, greatly reduced vegetation heterogeneity under a single SIF footprint. [Conclusions] The BPSIF proposed has significantly enhancing the regional and temporal continuity of OCO-2 SIF while preserving the time and spatial attributes contained in the original SIF dataset. Within the study area, BPSIF exhibits a significantly improved correlation with MODIS GPP compared to the original OCO-2 SIF. The proposed OCO-2 SIF data reconstruction method in this study holds the potential to provide a more reliable SIF dataset. This dataset has the potential to drive further understanding of soybean SIF at finer spatial and temporal scales, as well as find its relationship with soybean GPP. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Rice Disease and Pest Recognition Method Integrating ECA Mechanism and DenseNet201 PAN Chenlu, ZHANG Zhenghua, GUI Wenhao, MA Jiajun, YAN Chenxi, ZHANG Xiaomin Smart Agriculture    2023, 5 (2): 45-55.   DOI: 10.12133/j.smartag.SA202305002 Abstract （585）   HTML （74）    PDF（pc） （1686KB）（4083）       Knowledge map    Save [Objective] To address the problems of low efficiency and high cost of traditional manual identification of pests and diseases, improve the automatic recognition of pests and diseases by introducing advanced technical means, and provide feasible technical solutions for agricultural pest and disease monitoring and prevention and control, a rice image recognition model GE-DenseNet (G-ECA DenseNet) based on improved ECA (Efficient Channel Attention) mechanism with DenseNet201 was proposed. [Methods] The leaf images of three pests and diseases, namely, brownspot, hispa, leafblast and healthy rice were selected as experimental materials. The images were captured at the Zhuanghe Rice Professional Cooperative in Yizheng, Jiangsu Province, and the camera was used to manually take pictures from multiple angles such as the top and side of rice every 2 h, thus acquiring 1250 images of rice leaves under different lighting conditions, different perspectives, and different shading environments. In addition, samples about pests and diseases were collected in the Kaggle database. There were 1488 healthy leaves, 523 images of brownspot, 565 images of hispa, and 779 images of leafblast in the dataset. Since the original features of the pest and disease data were relatively close, firstly, the dataset was divided into a training set and a test set according to the ratio of 9:1, and then data enhancement was performed on the training set. A region of interest (ROI) was randomly selected to achieve a local scale of 1.1 to 1.25 for the sample images of the dataset, thus simulating the situation that only part of the leaves were captured in the actual shooting process due to the different distance of the plants from the camera. In addition, a random rotation of a certain angle was used to crop the image to simulate the different angles of the leaves. Finally, the experimental training set contains 18,018 images and the test set contains 352 images. The GE-DenseNet model firstly introduces the idea of Ghost module on the ECA attention mechanism to constitute the G-ECA Layer structure, which replaces the convolution operation with linear transformation to perform efficient fusion of channel features while avoiding dimensionality reduction when learning channel attention information and effectively enhancing its ability to extract features. Secondly, since the original Dense Block only considered the correlation between different layers and ignores the extraction of important channel information in the image recognition process, introducing G-ECA Layer before the original Dense Block of DenseNet201 gives the model a better channel feature extraction capability and thus improved the recognition accuracy. Due to the small dataset used in the experiment, the weight parameters of DenseNet201 pre-trained on the ImageNet dataset were migrated to GE-DenseNet. During the training process, the BatchSize size was set to 32, the number of iterations (Epoch) was set to 50, and the Focal Loss function was used to solve the problem of unbalanced samples for each classification. Meanwhile, the adaptive moment estimation (Adam) optimizer was used to avoid the problem of drastic gradient changes in back propagation due to random initialization of some weights at the early stage of model training, which weakened the uncertainty of network training to a certain extent. [Results and Discussions] Experimental tests were conducted on a homemade dataset of rice pests and diseases, and the recognition accuracy reached 83.52%. Comparing the accuracy change graphs and loss rate change graphs of GE-DenseNet and DenseNet201, it could be found that the proposed method in this study was effective in training stability, which could accelerate the speed of model convergence and improve the stability of the model, making the network training process more stable. And observing the visualization results of GE-DenseNet and DenseNet201 corresponding feature layers, it could be found that the features were more densely reflected around the pests and diseases after adding the G-ECA Layer structure. From the ablation comparison experiments of the GE-DenseNet model, it could be obtained that the model accuracy increased by 2.27% after the introduction of the Focal Loss function with the G-ECA Layer layer. Comparing the proposed model with the classical NasNet (4@1056), VGG-16 and ResNet50 models, the classification accuracy increased by 6.53%, 4.83% and 3.69%, respectively. Compared with the original DenseNet201, the recognition accuracy of hispa improved 20.32%. [Conclusions] The experimental results showed that the addition of G-ECA Layer structure enables the model to more accurately capture feature information suitable for rice pest recognition, thus enabling the GE-DenseNet model to achieve more accurate recognition of different rice pest images. This provides reliable technical support for timely pest and disease control, reducing crop yield loss and pesticide use. Future research can lighten the model and reduce its size without significantly reducing the recognition accuracy, so that it can be deployed in UAVs, tractors and various distributed image detection edge devices to facilitate farmers to conduct real-time inspection of farmland and further enhance the intelligence of agricultural production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Autonomous Navigation and Automatic Target Spraying Robot for Orchards LIU Limin, HE Xiongkui, LIU Weihong, LIU Ziyan, HAN Hu, LI Yangfan Smart Agriculture    2022, 4 (3): 63-74.   DOI: 10.12133/j.smartag.SA202207008 Abstract （1087）   HTML （88）    PDF（pc） （1905KB）（4028）       Knowledge map    Save To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02°. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss. Reference | Related Articles | Metrics | Comments（0）

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Extraction of Potato Plant Phenotypic Parameters Based on Multi-Source Data HU Songtao, ZHAI Ruifang, WANG Yinghua, LIU Zhi, ZHU Jianzhong, REN He, YANG Wanneng, SONG Peng Smart Agriculture    2023, 5 (1): 132-145.   DOI: 10.12133/j.smartag.SA202302009 Abstract （876）   HTML （130）    PDF（pc） （2649KB）（3906）       Knowledge map    Save Crops have diverse structures and complex growth environments. RGB image data can reflect the texture and color features of plants accurately, while 3D data contains information about crop volume. The combination of RGB image and 3D point cloud data can achieve the extraction of two-dimensional and three-dimensional phenotypic parameters of crops, which is of great significance for the research of phenomics methods. In this study, potatoe plants were chosen as the research subject, and RGB cameras and laser scanners were used to collect 50 potato RGB images and 3D laser point cloud data. The segmentation accuracy of four deep learning semantic segmentation methods, OCRNet, UpNet, PaNet, and DeepLab v3+, were compared and analyzed for the RGB images. OCRNet, which demonstrated higher accuracy, was used to perform semantic segmentation on top-view RGB images of potatoes. Mean shift clustering algorithm was optimized for laser point cloud data processing, and single-plant segmentation of laser point cloud data was completed. Stem and leaf segmentation of single-plant potato point cloud data were accurately performed using Euclidean clustering and K-Means clustering algorithms. In addition, a strategy was proposed to establish a one-to-one correspondence between RGB images and point clouds of single-plant potatoes using pot numbering. 8 2D phenotypic parameters and 10 3D phenotypic parameters, including maximum width, perimeter, area, plant height, volume, leaf length, and leaf width, etc., were extracted from RGB images and laser point clouds, respectively. Finally, the accuracy of three representative and easily measurable phenotypic parameters, leaf number, plant height, and maximum width were evaluated. The mean absolute percentage errors (MAPE) were 8.6%, 8.3% and 6.0%, respectively, while the root mean square errors (RMSE) were 1.371 pieces, 3.2 cm and 1.86 cm, respectively, and the determination coefficients (R2) were 0.93, 0.95 and 0.91, respectively. The research results indicated that the extracted phenotype parameters can accurately and efficiently reflect the growth status of potatoes. Combining the RGB image data of potatoes with three-dimensional laser point cloud data can fully exploit the advantages of the rich texture and color characteristics of RGB images and the volumetric information provided by three-dimensional point clouds, achieving non-destructive, efficient, and high-precision extraction of two-dimensional and three-dimensional phenotype parameters of potato plants. The achievements of this study could not only provide important technical support for the cultivation and breeding of potatoes but also provide strong support for phenotype-based research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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MSH-YOLOv8: Mushroom Small Object Detection Method with Scale Reconstruction and Fusion YE Dapeng, JING Jun, ZHANG Zhide, LI Huihuang, WU Haoyu, XIE Limin Smart Agriculture    2024, 6 (5): 139-152.   DOI: 10.12133/j.smartag.SA202404002 Abstract （650）   HTML （82）    PDF（pc） （2660KB）（3808）       Knowledge map    Save [Objective] Traditional object detection algorithms applied in the agricultural field, such as those used for crop growth monitoring and harvesting, often suffer from insufficient accuracy. This is particularly problematic for small crops like mushrooms, where recognition and detection are more challenging. The introduction of small object detection technology promises to address these issues, potentially enhancing the precision, efficiency, and economic benefits of agricultural production management. However, achieving high accuracy in small object detection has remained a significant challenge, especially when dealing with varying image sizes and target scales. Although the YOLO series models excel in speed and large object detection, they still have shortcomings in small object detection. To address the issue of maintaining high accuracy amid changes in image size and target scale, a novel detection model, Multi-Strategy Handling YOLOv8 (MSH-YOLOv8), was proposed. [Methods] The proposed MSH-YOLOv8 model builds upon YOLOv8 by incorporating several key enhancements aimed at improving sensitivity to small-scale targets and overall detection performance. Firstly, an additional detection head was added to increase the model's sensitivity to small objects. To address computational redundancy and improve feature extraction, the Swin Transformer detection structure was introduced into the input module of the head network, creating what was termed the "Swin Head (SH)". Moreover, the model integrated the C2f_Deformable convolutionv4 (C2f_DCNv4) structure, which included deformable convolutions, and the Swin Transformer encoder structure, termed "Swinstage", to reconstruct the YOLOv8 backbone network. This optimization enhanced feature propagation and extraction capabilities, increasing the network's ability to handle targets with significant scale variations. Additionally, the normalization-based attention module (NAM) was employed to improve performance without compromising detection speed or computational complexity. To further enhance training efficacy and convergence speed, the original loss function CIoU was replaced with wise-intersection over union (WIoU) Loss. Furthermore, experiments were conducted using mushrooms as the research subject on the open Fungi dataset. Approximately 200 images with resolution sizes around 600×800 were selected as the main research material, along with 50 images each with resolution sizes around 200×400 and 1 000×1 200 to ensure representativeness and generalization of image sizes. During the data augmentation phase, a generative adversarial network (GAN) was utilized for resolution reconstruction of low-resolution images, thereby preserving semantic quality as much as possible. In the post-processing phase, dynamic resolution training, multi-scale testing, soft non-maximum suppression (Soft-NMS), and weighted boxes fusion (WBF) were applied to enhance the model's small object detection capabilities under varying scales. [Results and Discussions] The improved MSH-YOLOv8 achieved an average precision at 50% (AP50) intersection over union of 98.49% and an AP@50-95 of 75.29%, with the small object detection metric APs reaching 39.73%. Compared to mainstream models like YOLOv8, these metrics showed improvements of 2.34%, 4.06% and 8.55%, respectively. When compared to the advanced TPH-YOLOv5 model, the improvements were 2.14%, 2.76% and 6.89%, respectively. The ensemble model, MSH-YOLOv8-ensemble, showed even more significant improvements, with AP50 and APs reaching 99.14% and 40.59%, respectively, an increase of 4.06% and 8.55% over YOLOv8. These results indicate the robustness and enhanced performance of the MSH-YOLOv8 model, particularly in detecting small objects under varying conditions. Further application of this methodology on the Alibaba Cloud Tianchi databases "Tomato Detection" and "Apple Detection" yielded MSH-YOLOv8-t and MSH-YOLOv8-a models (collectively referred to as MSH-YOLOv8). Visual comparison of detection results demonstrated that MSH-YOLOv8 significantly improved the recognition of dense and blurry small-scale tomatoes and apples. This indicated that the MSH-YOLOv8 method possesses strong cross-dataset generalization capability and effectively recognizes small-scale targets. In addition to quantitative improvements, qualitative assessments showed that the MSH-YOLOv8 model could handle complex scenarios involving occlusions, varying lighting conditions, and different growth stages of the crops. This demonstrates the practical applicability of the model in real-world agricultural settings, where such challenges are common. [Conclusions] The MSH-YOLOv8 improvement method proposed in this study effectively enhances the detection accuracy of small mushroom targets under varying image sizes and target scales. This approach leverages multiple strategies to optimize both the architecture and the training process, resulting in a robust model capable of high-precision small object detection. The methodology's application to other datasets, such as those for tomato and apple detection, further underscores its generalizability and potential for broader use in agricultural monitoring and management tasks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Advances and Development Trend of Mountainous Tractor Leveling and Anti-Rollover System MU Xiaodong, YANG Fuzeng, DUAN Luojia, LIU Zhijie, SONG Zhuoying, LI Zonglin, GUAN Shouqing Smart Agriculture    2024, 6 (3): 1-16.   DOI: 10.12133/j.smartag.SA202312015 Abstract （967）   HTML （111）    PDF（pc） （2448KB）（3743）       Knowledge map    Save [Significance] The mechanization, automation and intelligentization of agricultural equipment are key factors to improve operation efficiency, free up labor force and promote the sustainable development of agriculture. It is also the hot spot of research and development of agricultural machinery industry in the future. In China, hills and mountains serves as vital production bases for agricultural products, accounting for about 70% of the country's land area. In addition, these regions face various environmental factors such as steep slopes, narrow road, small plots, complex terrain and landforms, as well as harsh working environment. Moreover, there is a lack of reliable agricultural machinery support across various production stages, along with a shortage of theoretical frameworks to guide the research and development of agricultural machinery tailored to hilly and mountainous locales. [Progress] This article focuses on the research advances of tractor leveling and anti-overturning systems in hilly and mountainous areas, including tractor body, cab and seat leveling technology, tractor rear suspension and implement leveling slope adaptive technology, and research progress on tractor anti-overturning protection devices and warning technology. The vehicle body leveling mechanism can be roughly divided into five types based on its different working modes: parallel four bar, center of gravity adjustable, hydraulic differential high, folding and twisting waist, and omnidirectional leveling. These mechanisms aim to address the issue of vehicle tilting and easy overturning when traversing or working on sloping or rugged roads. By keeping the vehicle body posture horizontal or adjusting the center of gravity within a stable range, the overall driving safety of the vehicle can be improved to ensure the accuracy of operation. Leveling the driver's cab and seats can mitigate the lateral bumps experienced by the driver during rough or sloping operations, reducing driver fatigue and minimizing strain on the lumbar and cervical spine, thereby enhancing driving comfort. The adaptive technology of tractor rear suspension and implement leveling on slopes can ensure that the tractor maintains consistent horizontal contact with the ground in hilly and mountainous areas, avoiding changes in the posture of the suspended implement with the swing of the body or the driving path, which may affect the operation effect. The tractor rollover protection device and warning technology have garnered significant attention in recent years. Prioritizing driver safety, rollover warning system can alert the driver in advance of the dangerous state of the tractor, automatically adjust the vehicle before rollover, or automatically open the rollover protection device when it is about to rollover, and timely send accident reports to emergency contacts, thereby ensuring the safety of the driver to the greatest extent possible. [Conclusions and Prospects] The future development directions of hill and mountain tractor leveling, anti-overturning early warning, unmanned, automatic technology were looked forward: Structure optimization, high sensitivity, good stability of mountain tractor leveling system research; Study on copying system of agricultural machinery with good slope adaptability; Research on anti-rollover early warning technology of environment perception and automatic interference; Research on precision navigation technology, intelligent monitoring technology and remote scheduling and management technology of agricultural machinery; Theoretical study on longitudinal stability of sloping land. This review could provide reference for the research and development of high reliability and high safety mountain tractor in line with the complex working environment in hill and mountain areas. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Paradigm Theory and Judgment Conditions of Geophysical Parameter Retrieval Based on Artificial Intelligence MAO Kebiao, ZHANG Chenyang, SHI Jiancheng, WANG Xuming, GUO Zhonghua, LI Chunshu, DONG Lixin, WU Menxin, SUN Ruijing, WU Shengli, JI Dabin, JIANG Lingmei, ZHAO Tianjie, QIU Yubao, DU Yongming, XU Tongren Smart Agriculture    2023, 5 (2): 161-171.   DOI: 10.12133/j.smartag.SA202304013 Abstract （643）   HTML （62）    PDF（pc） （1400KB）（3712）       Knowledge map    Save Objective Deep learning is one of the most important technologies in the field of artificial intelligence, which has sparked a research boom in academic and engineering applications. It also shows strong application potential in remote sensing retrieval of geophysical parameters. The cross-disciplinary research is just beginning, and most deep learning applications in geosciences are still "black boxes", with most applications lacking physical significance, interpretability, and universality. In order to promote the application of artificial intelligence in geosciences and agriculture and cultivate interdisciplinary talents, a paradigm theory for geophysical parameter retrieval based on artificial intelligence coupled physics and statistical methods was proposed in this research. Methods The construction of the retrieval paradigm theory for geophysical parameters mainly included three parts: Firstly, physical logic deduction was performed based on the physical energy balance equation, and the inversion equation system was constructed theoretically which eliminated the ill conditioned problem of insufficient equations. Then, a fuzzy statistical method was constructed based on physical deduction. Representative solutions of physical methods were obtained through physical model simulation, and other representative solutions as the training and testing database for deep learning were obtained using multi-source data. Finally, deep learning achieved the goal of coupling physical and statistical methods through the use of representative solutions from physical and statistical methods as training and testing databases. Deep learning training and testing were aimed at obtaining curves of solutions from physical and statistical methods, thereby making deep learning physically meaningful and interpretable. Results and Discussions The conditions for determining the formation of a universal and physically interpretable paradigm were: (1) There must be a causal relationship between input and output variables (parameters); (2) In theory, a closed system of equations (with unknowns less than or equal to the number of equations) can be constructed between input and output variables (parameters), which means that the output parameters can be uniquely determined by the input parameters. If there is a strong causal relationship between input parameters (variables) and output parameters (variables), deep learning can be directly used for inversion. If there is a weak correlation between the input and output parameters, prior knowledge needs to be added to improve the inversion accuracy of the output parameters. The MODIS thermal infrared remote sensing data were used to retrieve land surface temperature, emissivity, near surface air temperature and atmospheric water vapor content as a case to prove the theory. When there was strong correlation between output parameters (LST and LSE) and input variables (BTi), using deep learning coupled with physical and statistical methods could obtain very high accuracy. When there was a weak correlation between the output parameter (NSAT) and the input variable (BTi), adding prior knowledge (LST and LSE) could improve the inversion accuracy and stability of the output parameter (NSAT). When there was partial strong correlation (WVC and BTi), adding prior knowledge (LST and LSE) could slightly improve accuracy and stability, but the error of prior knowledge (LST and LSE) may bring uncertainty, so prior knowledge could also be omitted. According to the inversion analysis of geophysical parameters of MODIS sensor thermal infrared band, bands 27, 28, 29 and 31 were more suitable for inversion of atmospheric water vapor content, and bands 28, 29, 31 and 32 were more suitable for inversion of surface temperature, Emissivity and near surface air temperature. If someone want to achieve the highest accuracy of four parameters, it was recommended to design the instrument with five bands (27, 28, 29, 31, 32) which were most suitable. If only four thermal infrared bands were designed, bands 27, 28, 31, and 32 should be given priority consideration. From the results of land surface temperature, emissivity, near surface air temperature and atmospheric water vapor content retrieved from MODIS data using this theory, it was not only more accurate than traditional methods, but also could reduce some bands, reduce satellite load and improve satellite life. Especially, this theoretical method overcomes the influence of the MODIS official algorithm (day/night algorithm) on sudden changes in surface types and long-term lack of continuous data, which leads to unstable accuracy of the inversion product. The analysis results showed that the proposed theory and conditions are feasible, and the accuracy and applicability were better than traditional methods. The theory and judgment conditions of geophysical parameter retrieval paradigms were also applicable for target recognition such as remote sensing classification, but it needed to be interpreted from a different perspective. For example, the feature information extracted by different convolutional kernels must be able to uniquely determine the target. Under satisfying with the conditions of paradigm theory, the inversion of geophysical parameters based on artificial intelligence is the best choice. Conclusions The geophysical parameter retrieval paradigm theory based on artificial intelligence proposed in this study can overcome the shortcomings of traditional retrieval methods, especially remote sensing parameter retrieval, which simplify the inversion process and improve the inversion accuracy. At the same time, it can optimize the design of satellite sensors. The proposal of this theory is of milestone significance in the history of geophysical parameter retrieval. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Automatic Measurement of Multi-Posture Beef Cattle Body Size Based on Depth Image YE Wenshuai, KANG Xi, HE Zhijiang, LI Mengfei, LIU Gang Smart Agriculture    2022, 4 (4): 144-155.   DOI: 10.12133/j.smartag.SA202210001 Abstract （1430）   HTML （108）    PDF（pc） （1532KB）（3494）       Knowledge map    Save Beef cattle in the farm are active, which leads the collection of posture of the beef cattle changeable, so it is difficult to automatically measure the body size of the beef cattle. Aiming at the above problems, an automatic measurement method for beef cattle's body size under multi-pose was proposed by analyzing the skeleton features of beef cattle head and the edge contour features of beef cattle images. Firstly, the consumer-grade depth camera Azure Kinect DK was used to collect the top-view depth video data directly above the beef cattle and the video data were divided into frames to obtain the original depth image. Secondly, the original depth image was processed by shadow interpolation, normalization, image segmentation and connected domain to remove the complex background and obtain the target image containing only beef cattle. Thirdly, the Zhang-Suen algorithm was used to extract the beef cattle skeleton of the target image, and calculated the intersection points and endpoints of the skeleton, so as to analyze the characteristics of the beef cattle head to determine the head removal point, and to remove the beef cattle head information from the image. Finally, the curvature curve of the beef cattle profile was obtained by the improved U-chord curvature method. The body measurement points were determined according to the curvature value and converted into three-dimensional spaces to calculate the body size parameters. In this paper, the postures of beef cattle, which were analyzed by a large amount of depth image data, were divided into left crooked, right crooked, correct posture, head down and head up, respectively. The test results showed that the head removal method proposed based on the skeleton in multiple postures hads head removel success rate higher than 92% in the five postures. Using the body measurement point extraction method based on the improved U-chord curvature proposed, the average absolute error of body length measurement was 2.73 cm, the average absolute error of body height measurement was 2.07 cm, and the average absolute error of belly width measurement was 1.47 cm. The method provides a better way to achieve the automatic measurement of beef cattle body size in multiple poses. Reference | Related Articles | Metrics | Comments（0）

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Key Technologies and Equipment for Smart Orchard Construction and Prospects HAN Leng, HE Xiongkui, WANG Changling, LIU Yajia, SONG Jianli, QI Peng, LIU Limin, LI Tian, ZHENG Yi, LIN Guihai, ZHOU Zhan, HUANG Kang, WANG Zhong, ZHA Hainie, ZHANG Guoshan, ZHOU Guotao, MA Yong, FU Hao, NIE Hongyuan, ZENG Aijun, ZHANG Wei Smart Agriculture    2022, 4 (3): 1-11.   DOI: 10.12133/j.smartag.SA200201014 Abstract （2150）   HTML （507）    PDF（pc） （2824KB）（3448）       Knowledge map    Save Traditional orchard production is facing problems of labor shortage due to the aging, difficulties in the management of agricultural equipment and production materials, and low production efficiency which can be expected to be solved by building a smart orchard that integrates technologies of Internet of Things(IoT), big data, equipment intelligence, et al. In this study, based on the objectives of full mechanization and intelligent management, a smart orchard was built in Pinggu district, an important peaches and pears fruit producing area of Beijing. The orchard covers an aera of more than 30 hm2 in Xiying village, Yukou town. In the orchard, more than 10 kinds of information acquisition sensors for pests, diseases, water, fertilizers and medicines are applied, 28 kinds of agricultural machineries with intelligent technical support are equipped. The key technologies used include: intelligent information acquisition system, integrated water and fertilizer management system and intelligent pest management system. The intelligent operation equipment system includes: unmanned lawn mower, intelligent anti-freeze machine, trenching and fertilizer machine, automatic driving crawler, intelligent profiling variable sprayer, six-rotor branch-to-target drone, multi-functional picking platform and finishing and pruning machine, etc. At the same time, an intelligent management platform has been built in the smart orchard. The comparison results showed that, smart orchard production can reduce labor costs by more than 50%, save pesticide dosage by 30% ~ 40%, fertilizer dosage by 25% ~ 35%, irrigation water consumption by 60% ~ 70%, and comprehensive economic benefits increased by 32.5%. The popularization and application of smart orchards will further promote China's fruit production level and facilitate the development of smart agriculture in China. Reference | Related Articles | Metrics | Comments（0）

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A Lightweight Model for Detecting Small Targets of Litchi Pests Based on Improved YOLOv10n LI Zusheng, TANG Jishen, KUANG Yingchun Smart Agriculture    2025, 7 (2): 146-159.   DOI: 10.12133/j.smartag.SA202412003 Abstract （760）   HTML （106）    PDF（pc） （2262KB）（3282）       Knowledge map    Save Objective The accuracy of identifying litchi pests is crucial for implementing effective control strategies and promoting sustainable agricultural development. However, the current detection of litchi pests is characterized by a high percentage of small targets, which makes target detection models challenging in terms of accuracy and parameter count, thus limiting their application in real-world production environments. To improve the identification efficiency of litchi pests, a lightweight target detection model YOLO-LP (YOLO-Litchi Pests) based on YOLOv10n was proposed. The model aimed to enhance the detection accuracy of small litchi pest targets in multiple scenarios by optimizing the network structure and loss function, while also reducing the number of parameters and computational costs. Methods Two classes of litchi insect pests (Cocoon and Gall) images were collected as datasets for modeling in natural scenarios (sunny, cloudy, post-rain) and laboratory environments. The original data were expanded through random scaling, random panning, random brightness adjustments, random contrast variations, and Gaussian blurring to balance the category samples and enhance the robustness of the model, generating a richer dataset named the CG dataset (Cocoon and Gall dataset). The YOLO-LP model was constructed after the following three improvements. Specifically, the C2f module of the backbone network (Backbone) in YOLOv10n was optimized and the C2f_GLSA module was constructed using the global-to-local spatial aggregation (GLSA) module to focus on small targets and enhance the differentiation between the targets and the backgrounds, while simultaneously reducing the number of parameters and computation. A frequency-aware feature fusion module (FreqFusion) was introduced into the neck network (Neck) of YOLOv10n and a frequency-aware path aggregation network (FreqPANet) was designed to reduce the complexity of the model and address the problem of fuzzy and shifted target boundaries. The SCYLLA-IoU (SIoU) loss function replaced the Complete-IoU (CIoU) loss function from the baseline model to optimize the target localization accuracy and accelerate the convergence of the training process. Results and Discussions YOLO-LP achieved 90.9%, 62.2%, and 59.5% for AP50, AP50:95, and AP-Small50:95 in the CG dataset, respectively, and 1.9%, 1.0%, and 1.2% higher than the baseline model. The number of parameters and the computational costs were reduced by 13% and 17%, respectively. These results suggested that YOLO-LP had a high accuracy and lightweight design. Comparison experiments with different attention mechanisms validated the effectiveness of the GLSA module. After the GLSA module was added to the baseline model, AP50, AP50:95, and AP-Small50:95 achieved the highest performance in the CG dataset, reaching 90.4%, 62.0%, and 59.5%, respectively. Experiment results comparing different loss functions showed that the SIoU loss function provided better fitting and convergence speed in the CG dataset. Ablation test results revealed that the validity of each model improvement and the detection performance of any combination of the three improvements was significantly better than the baseline model in the YOLO-LP model. The performance of the models was optimal when all three improvements were applied simultaneously. Compared to several mainstream models, YOLO-LP exhibited the best overall performance, with a model size of only 5.1 MB, 1.97 million parameters (Params), and a computational volume of 5.4 GFLOPs. Compared to the baseline model, the detection of the YOLO-LP performance was significantly improved across four multiple scenarios. In the sunny day scenario, AP50, AP50:95, and AP-Small50:95 increased by 1.9%, 1.0 %, and 2.0 %, respectively. In the cloudy day scenario, AP50, AP50:95, and AP-Small50:95 increased by 2.5%, 1.3%, and 1.3%, respectively. In the post-rain scenario, AP50, AP50:95, and AP-Small50:95 increased by 2.0%, 2.4%, and 2.4%, respectively. In the laboratory scenario, only AP50 increased by 0.7% over the baseline model. These findings indicated that YOLO-LP achieved higher accuracy and robustness in multi-scenario small target detection of litchi pests. Conclusions The proposed YOLO-LP model could improve detection accuracy and effectively reduce the number of parameters and computational costs. It performed well in small target detection of litchi pests and demonstrated strong robustness across different scenarios. These improvements made the model more suitable for deployment on resource-constrained mobile and edge devices. The model provided a valuable technical reference for small target detection of litchi pests in various scenarios. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Infield Corn Kernel Detection and Counting Based on Multiple Deep Learning Networks LIU Xiaohang, ZHANG Zhao, LIU Jiaying, ZHANG Man, LI Han, FLORES Paulo, HAN Xiongzhe Smart Agriculture    2022, 4 (4): 49-60.   DOI: 10.12133/j.smartag.SA202207004 Abstract （1521）   HTML （145）    PDF（pc） （3336KB）（3178）       Knowledge map    Save Machine vision has been increasingly used for agricultural sensing tasks. The detection method based on deep learning for infield corn kernels can improve the detection accuracy. In order to obtain the number of lost corn kernels quickly and accurately after the corn harvest, and evaluate the corn harvest combine performance on grain loss, the method of directly using deep learning technology to count corn kernels in the field was developed and evaluated. Firstly, an RGB camera was used to collect image with different backgrounds and illuminations, and the datasets were generated. Secondly, different target detection networks for kernel recognition were constructed, including Mask R-CNN, EfficientDet-D5, YOLOv5-L and YOLOX-L, and the collected 420 effective images were used to train, verify and test each model. The number of images in train, verify and test datasets were 200, 40 and 180, respectively. Finally, the counting performances of different models were evaluated and compared according to the recognition results of test set images. The experimental results showed that among the four models, YOLOv5-L had overall the best performance, and could reliably identify corn kernels under different scenes and light conditions. The average precision (AP) value of the model for the image detection of the test set was 78.3%, and the size of the model was 89.3 MB. The correct rate of kernel count detection in four scenes of non-occlusion, surface mid-level-occlusion, surface severe-occlusion and aggregation were 98.2%, 95.5%, 76.1% and 83.3%, respectively, and F1 values were 94.7%, 93.8%, 82.8% and 87%, respectively. The overall detection correct rate and F1 value of the test set were 90.7% and 91.1%, respectively. The frame rate was 55.55 f/s, and the detection and counting performance were better than Mask R-CNN, EfficientDet-D5 and YOLOX-L networks. The detection accuracy was improved by about 5% compared with the second best performance of Mask R-CNN. With good precision, high throughput, and proven generalization, YOLOv5-L can realize real-time monitoring of corn harvest loss in practical operation. Reference | Related Articles | Metrics | Comments（0）

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Three-Dimensional Dynamic Growth and Yield Simulation Model of Daylily Plants ZHANG Yue, LI Weijia, HAN Zhiping, ZHANG Kun, LIU Jiawen, HENKE Michael Smart Agriculture    2024, 6 (2): 140-153.   DOI: 10.12133/j.smartag.SA202310011 Abstract （446）   HTML （39）    PDF（pc） （2579KB）（3084）       Knowledge map    Save [Objective] The daylily, a perennial herb in the lily family, boasts a rich nutritional profile. Given its economic importance, enhancing its yield is a crucial objective. However, current research on daylily cultivation is limited, especially regarding three-dimensional dynamic growth simulation of daylily plants. In order to establish a technological foundation for improved cultivation management, growth dynamics prediction, and the development of plant variety types in daylily crops, this study introduces an innovative three-dimensional dynamic growth and yield simulation model for daylily plants. [Methods] The open-source GroIMP software platform was used to simulate and visualize three-dimensional scenes. With Datong daylily, the primary cultivated variety of daylily in the Datong area, as the research subject, a field experiment was conducted from March to September 2022, which covered the growth season of daylily. Through actual cultivation experiment measurements, morphological data and leaf photosynthetic physiological parameters of daylily leaves, flower stems, flower buds, and other organs were collected. The functional-structural plant model (FSPM) platform's three-dimensional modeling technology was employed to establish the Cloud Cover-based solar radiation models (CSRMs) and the Farquhar, von Camerer, and Berry model (FvCB model) suitable for daylily. Moreover, based on the source-sink relationship of daylily, the carbon allocation model of daylily photosynthetic products was developed. By using the β growth function, the growth simulation model of daylily organs was constructed, and the daily morphological data of daylily during the growth period were calculated, achieving the three-dimensional dynamic growth and yield simulation of daylily plants. Finally, the model was validated with measured data. [Results and Discussions] The coefficient of determination (R2) between the measured and simulated outdoor surface solar radiation was 0.87, accompanied by a Root Mean Squared Error (RMSE) of 28.52 W/m2. For the simulated model of each organ of the daylily plant, the R2 of the measured against the predicted values ranged from 0.896 to 0.984, with an RMSE varying between 1.4 and 17.7 cm. The R2 of the average flower bud yield simulation was 0.880, accompanied by an RMSE of 0.5 g. The overall F-value spanned from 82.244 to 1 168.533, while the Sig. value was consistently below the 0.05 significance level, suggesting a robust fit and statistical significance for the aforementioned models. Subsequently, a thorough examination of the light interaction, temperature influences, and photosynthetic attributes of daylily leaves throughout their growth cycle was carried out. The findings revealed that leaf nutrition growth played a pivotal role in the early phase of daylily's growth, followed by the contribution of leaf and flower stem nutrition in the middle stage, and finally the growth of daylily flower buds, which is the crucial period for yield formation, in the later stages. Analyzing the photosynthetic traits of daylily leaves comprehensively, it was observed that the photosynthetic rate was relatively low in the early spring as the new leaves were initially emerging and reached a plateau during the summer. Considering real-world climate conditions, the actual net photosynthetic rate was marginally lower than the rate verified under optimal conditions, with the simulated net assimilation rate typically ranging from 2 to 4 μmol CO2/(m2·s). [Conclusions] The three-dimensional dynamic growth model of daylily plants proposed in this study can faithfully articulate the growth laws and morphological traits of daylily plants across the three primary growth stages. This model not only illustrates the three-dimensional dynamic growth of daylily plants but also effectively mimics the yield data of daylily flower buds. The simulation outcomes concur with actual conditions, demonstrating a high level of reliability. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Identification Method of Wheat Grain Phenotype Based on Deep Learning of ImCascade R-CNN PAN Weiting, SUN Mengli, YUN Yan, LIU Ping Smart Agriculture    2023, 5 (3): 110-120.   DOI: 10.12133/j.smartag.SA202304006 Abstract （596）   HTML （69）    PDF（pc） （1664KB）（3072）       Knowledge map    Save [Objective] Wheat serves as the primary source of dietary carbohydrates for the human population, supplying 20% of the required caloric intake. Currently, the primary objective of wheat breeding is to develop wheat varieties that exhibit both high quality and high yield, ensuring an overall increase in wheat production. Additionally, the consideration of phenotype parameters, such as grain length and width, holds significant importance in the introduction, screening, and evaluation of germplasm resources. Notably, a noteworthy positive association has been observed between grain size, grain shape, and grain weight. Simultaneously, within the scope of wheat breeding, the occurrence of inadequate harvest and storage practices can readily result in damage to wheat grains, consequently leading to a direct reduction in both emergence rate and yield. In essence, the integrity of wheat grains directly influences the wheat breeding process. Nevertheless, distinguishing between intact and damaged grains remains challenging due to the minimal disparities in certain characteristics, thereby impeding the accurate identification of damaged wheat grains through manual means. Consequently, this study aims to address this issue by focusing on the detection of wheat kernel integrity and completing the attainment of grain phenotype parameters. [Methods] This study presented an enhanced approach for addressing the challenges of low detection accuracy, unclear segmentation of wheat grain contour, and missing detection. The proposed strategy involves utilizing the Cascade Mask R-CNN model and replacing the backbone network with ResNeXt to mitigate gradient dispersion and minimize the model's parameter count. Furthermore, the inclusion of Mish as an activation function enhanced the efficiency and versatility of the detection model. Additionally, a multilayer convolutional structure was introduced in the detector to thoroughly investigate the latent features of wheat grains. The Soft-NMS algorithm was employed to identify the candidate frame and achieve accurate segmentation of the wheat kernel adhesion region. Additionally, the ImCascade R-CNN model was developed. Simultaneously, to address the issue of low accuracy in obtaining grain contour parameters due to disordered grain arrangement, a grain contour-based algorithm for parameter acquisition was devised. Wheat grain could be approximated as an oval shape, and the grain edge contour could be obtained according to the mask, the distance between the farthest points could be iteratively obtained as the grain length, and the grain width could be obtained according to the area. Ultimately, a method for wheat kernel phenotype identification was put forth. The ImCascade R-CNN model was utilized to analyze wheat kernel images, extracting essential features and determining the integrity of the kernels through classification and boundary box regression branches. The mask generation branch was employed to generate a mask map for individual wheat grains, enabling segmentation of the grain contours. Subsequently, the number of grains in the image was determined, and the length and width parameters of the entire wheat grain were computed. [Results and Discussions] In the experiment on wheat kernel phenotype recognition, a comparison and improvement were conducted on the identification results of the Cascade Mask R-CNN model and the ImCascade R-CNN model across various modules. Additionally, the efficacy of the model modification scheme was verified. The comparison of results between the Cascade Mask R-CNN model and the ImCascade R-CNN model served to validate the proposed model's ability to significantly decrease the missed detection rate. The effectiveness and advantages of the ImCascade R-CNN model were verified by comparing its loss value, P-R value, and mAP_50 value with those of the Cascade Mask R-CNN model. In the context of wheat grain identification and segmentation, the detection results of the ImCascade R-CNN model were compared to those of the Cascade Mask R-CNN and Deeplabv3+ models. The comparison confirmed that the ImCascade R-CNN model exhibited superior performance in identifying and locating wheat grains, accurately segmenting wheat grain contours, and achieving an average accuracy of 90.2% in detecting wheat grain integrity. These findings serve as a foundation for obtaining kernel contour parameters. The grain length and grain width exhibited average error rates of 2.15% and 3.74%, respectively, while the standard error of the aspect ratio was 0.15. The statistical analysis and fitting of the grain length and width, as obtained through the proposed wheat grain shape identification method, yielded determination coefficients of 0.9351 and 0.8217, respectively. These coefficients demonstrated a strong agreement with the manually measured values, indicating that the method is capable of meeting the demands of wheat seed testing and providing precise data support for wheat breeding. [Conclusions] The findings of this study can be utilized for the rapid and precise detection of wheat grain integrity and the acquisition of comprehensive grain contour data. In contrast to current wheat kernel recognition technology, this research capitalizes on enhanced grain contour segmentation to furnish data support for the acquisition of wheat kernel contour parameters. Additionally, the refined contour parameter acquisition algorithm effectively mitigates the impact of disordered wheat kernel arrangement, resulting in more accurate parameter data compared to existing kernel appearance detectors available in the market, providing data support for wheat breeding and accelerating the cultivation of high-quality and high-yield wheat varieties. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Automatic Measurement of Mongolian Horse Body Based on Improved YOLOv8n-pose and 3D Point Cloud Analysis LI Minghuang, SU Lide, ZHANG Yong, ZONG Zheying, ZHANG Shun Smart Agriculture    2024, 6 (4): 91-102.   DOI: 10.12133/j.smartag.SA202312027 Abstract （1236）   HTML （59）    PDF（pc） （2477KB）（3022）       Knowledge map    Save [Objective] There exist a high genetic correlation among various morphological characteristics of Mongolian horses. Utilizing advanced technology to obtain body structure parameters related to athletic performance could provide data support for breeding institutions to develop scientific breeding plans and establish the groundwork for further improvement of Mongolian horse breeds. However, traditional manual measurement methods are time-consuming, labor-intensive, and may cause certain stress responses in horses. Therefore, ensuring precise and effective measurement of Mongolian horse body dimensions is crucial for formulating early breeding plans. [Method] Video images of 50 adult Mongolian horses in the suitable breeding stage at the Inner Mongolia Agricultural University Horse Breeding Technical Center was first collected. Fifty images per horse were captured to construct the training and validation sets, resulting in a total of 2 500 high-definition RGB images of Mongolian horses, with an equal ratio of images depicting horses in motion and at rest. To ensure the model's robustness and considering issues such as angles, lighting, and image blurring during actual image capture, a series of enhancement algorithms were applied to the original dataset, expanding the Mongolian horse image dataset to 4 000 images. The YOLOv8n-pose was employed as the foundational keypoint detection model. Through the design of the C2f_DCN module, deformable convolution (DCNV2) was integrated into the C2f module of the Backbone network to enhance the model's adaptability to different horse poses in real-world scenes. Besides, an SA attention module was added to the Neck network to improve the model's focus on critical features. The original loss function was replaced with SCYLLA-IoU (SIoU) to prioritize major image regions, and a cosine annealing method was employed to dynamically adjust the learning rate during model training. The improved model was named DSS-YOLO (DCNv2-SA-SIoU-YOLO) network model. Additionally, a test set comprising 30 RGB-D images of mature Mongolian horses was selected for constructing body dimension measurement tasks. DSS-YOLO was used for keypoint detection of body dimensions. The 2D keypoint coordinates from RGB images were fused with corresponding depth values from depth images to obtain 3D keypoint coordinates, and Mongolian horse's point cloud information was transformed. Point cloud processing and analysis were performed using pass-through filtering, random sample consensus (RANSAC) shape fitting, statistical outlier filtering, and principal component analysis (PCA) coordinate system correction. Finally, body height, body oblique length, croup height, chest circumference, and croup circumference were automatically computed based on keypoint spatial coordinates. [Results and Discussion] The proposed DSS-YOLO model exhibited parameter and computational costs of 3.48 M and 9.1 G, respectively, with an average accuracy mAP0.5:0.95 reaching 92.5%, and a dDSS of 7.2 pixels. Compared to Hourglass, HRNet, and SimCC, mAP0.5:0.95 increased by 3.6%, 2.8%, and 1.6%, respectively. By relying on keypoint coordinates for automatic calculation of body dimensions and suggesting the use of a mobile least squares curve fitting method to complete the horse's hip point cloud, experiments involving 30 Mongolian horses showed a mean average error (MAE) of 3.77 cm and mean relative error (MRE) of 2.29% in automatic measurements. [Conclusions] The results of this study showed that DSS-YOLO model combined with three-dimensional point cloud processing methods can achieve automatic measurement of Mongolian horse body dimensions with high accuracy. The proposed measurement method can also be extended to different breeds of horses, providing technical support for horse breeding plans and possessing practical application value. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Multi-Class on-Tree Peach Detection Using Improved YOLOv5s and Multi-Modal Images LUO Qing, RAO Yuan, JIN Xiu, JIANG Zhaohui, WANG Tan, WANG Fengyi, ZHANG Wu Smart Agriculture    2022, 4 (4): 84-104.   DOI: 10.12133/j.smartag.SA202210004 Abstract （1717）   HTML （143）    PDF（pc） （3285KB）（3017）       Knowledge map    Save Accurate peach detection is a prerequisite for automated agronomic management, e.g., peach mechanical harvesting. However, due to uneven illumination and ubiquitous occlusion, it is challenging to detect the peaches, especially when the peaches are bagged in orchards. To this end, an accurate multi-class peach detection method was proposed by means of improving YOLOv5s and using multi-modal visual data for mechanical harvesting in this paper. RGB-D dataset with multi-class annotations of naked and bagging peach was proposed, including 4127 multi-modal images of corresponding pixel-aligned color, depth, and infrared images acquired with consumer-level RGB-D camera. Subsequently, an improved lightweight YOLOv5s (small depth) model was put forward by introducing a direction-aware and position-sensitive attention mechanism, which could capture long-range dependencies along one spatial direction and preserve precise positional information along the other spatial direction, helping the networks accurately detect peach targets. Meanwhile, the depthwise separable convolution was employed to reduce the model computation by decomposing the convolution operation into convolution in the depth direction and convolution in the width and height directions, which helped to speed up the training and inference of the network while maintaining accuracy. The comparison experimental results demonstrated that the improved YOLOv5s using multi-modal visual data recorded the detection mAP of 98.6% and 88.9% on the naked and bagging peach with 5.05 M model parameters in complex illumination and severe occlusion environment, increasing by 5.3% and 16.5% than only using RGB images, as well as by 2.8% and 6.2% when compared to YOLOv5s. As compared with other networks in detecting bagging peaches, the improved YOLOv5s performed best in terms of mAP, which was 16.3%, 8.1% and 4.5% higher than YOLOX-Nano, PP-YOLO-Tiny, and EfficientDet-D0, respectively. In addition, the proposed improved YOLOv5s model offered better results in different degrees than other methods in detecting Fuji apple and Hayward kiwifruit, verified the effectiveness on different fruit detection tasks. Further investigation revealed the contribution of each imaging modality, as well as the proposed improvement in YOLOv5s, to favorable detection results of both naked and bagging peaches in natural orchards. Additionally, on the popular mobile hardware platform, it was found out that the improved YOLOv5s model could implement 19 times detection per second with the considered five-channel multi-modal images, offering real-time peach detection. These promising results demonstrated the potential of the improved YOLOv5s and multi-modal visual data with multi-class annotations to achieve visual intelligence of automated fruit harvesting systems. Reference | Related Articles | Metrics | Comments（0）

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Remote Sensing Identification Method of Cultivated Land at Hill County of Sichuan Basin Based on Deep Learning LI Hao, DU Yuqiu, XIAO Xingzhu, CHEN Yanxi Smart Agriculture    2024, 6 (3): 34-45.   DOI: 10.12133/j.smartag.SA202308002 Abstract （432）   HTML （50）    PDF（pc） （2649KB）（3006）       Knowledge map    Save [Objective] To fully utilize and protect farmland and lay a solid foundation for the sustainable use of land, it is particularly important to obtain real-time and precise information regarding farmland area, distribution, and other factors. Leveraging remote sensing technology to obtain farmland data can meet the requirements of large-scale coverage and timeliness. However, the current research and application of deep learning methods in remote sensing for cultivated land identification still requires further improvement in terms of depth and accuracy. The objective of this study is to investigate the potential application of deep learning methods in remote sensing for identifying cultivated land in the hilly areas of Southwest China, to provide insights for enhancing agricultural land utilization and regulation, and for harmonizing the relationship between cultivated land and the economy and ecology. [Methods] Santai county, Mianyang city, Sichuan province, China (30°42'34"~31°26'35"N, 104°43'04"~105°18'13"E) was selected as the study area. High-resolution imagery from two scenes captured by the Gaofen-6 (GF-6) satellite served as the primary image data source. Additionally, 30-meter resolution DEM data from the United States National Aeronautics and Space Administration (NASA) in 2020 was utilized. A land cover data product, SinoLC-1, was also incorporated for comparative evaluation of the accuracy of various extraction methods' results. Four deep learning models, namely Unet, PSPNet, DeeplabV3+, and Unet++, were utilized for remote sensing land identification research in cultivated areas. The study also involved analyzing the identification accuracy of cultivated land in high-resolution satellite images by combining the results of the random forest (RF) algorithm along with the deep learning models. A validation dataset was constructed by randomly generating 1 000 vector validation points within the research area. Concurrently, Google Earth satellite images with a resolution of 0.3 m were used for manual visual interpretation to determine the land cover type of the pixels where the validation points are located. The identification results of each model were compared using a confusion matrix to compute five accuracy evaluation metrics: Overall accuracy (OA), intersection over union (IoU), mean intersection over union (MIoU), F1-Score, and Kappa Coefficient to assess the cultivated land identification accuracy of different models and data products. [Results and Discussions] The deep learning models displayed significant advances in accuracy evaluation metrics, surpassing the performance of traditional machine learning approaches like RF and the latest land cover product, SinoLC-1 Landcover. Among the models assessed, the UNet++ model performed the best, its F1-Score, IoU, MIoU, OA, and Kappa coefficient values were 0.92, 85.93%, 81.93%, 90.60%, and 0.80, respectively. DeeplabV3+, UNet, and PSPNet methods followed suit. These performance metrics underscored the superior accuracy of the UNet++ model in precisely identifying and segmenting cultivated land, with a remarkable increase in accuracy of nearly 20% than machine learning methods and 50% for land cover products. Four typical areas of town, water body, forest land and contiguous cultivated land were selected to visually compare the results of cultivated land identification results. It could be observed that the deep learning models generally exhibited consistent distribution patterns with the satellite imageries, accurately delineating the boundaries of cultivated land and demonstrating overall satisfactory performance. However, due to the complex features in remote sensing images, the deep learning models still encountered certain challenges of omission and misclassification in extracting cultivated land. Among them, the UNet++ model showed the closest overall extraction results to the ground truth and exhibited advantages in terms of completeness of cultivated land extraction, discrimination between cultivated land and other land classes, and boundary extraction compared to other models. Using the UNet++ model with the highest recognition accuracy, two types of images constructed with different features—solely spectral features and spectral combined with terrain features—were utilized for cultivated land extraction. Based on the three metrics of IoU, OA, and Kappa, the model incorporating both spectral and terrain features showed improvements of 0.98%, 1.10%, and 0.01% compared to the model using only spectral features. This indicated that fusing spectral and terrain features can achieve information complementarity, further enhancing the identification effectiveness of cultivated land. [Conclusions] This study focuses on the practicality and reliability of automatic cultivated land extraction using four different deep learning models, based on high-resolution satellite imagery from the GF-6 in Santai county in China. Based on the cultivated land extraction results in Santai county and the differences in network structures among the four deep learning models, it was found that the UNet++ model, based on UNet, can effectively improve the accuracy of cultivated land extraction by introducing the mechanism of skip connections. Overall, this study demonstrates the effectiveness and practical value of deep learning methods in obtaining accurate farmland information from high-resolution remote sensing imagery. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Advances in Forage Crop Growth Monitoring by UAV Remote Sensing ZHUO Yue, DING Feng, YAN Haijun, XU Jing Smart Agriculture    2022, 4 (4): 35-48.   DOI: 10.12133/j.smartag.SA202206004 Abstract （1277）   HTML （147）    PDF（pc） （863KB）（2967）       Knowledge map    Save Dynamic monitoring and quantitative estimation of forage crop growth are of great importance to the large-scale production of forage crop. UAV remote sensing has the advantages of high resolution, strong flexibility and low cost. In recent years, it has developed rapidly in the field of forage crop growth monitoring. In order to clarify the development status of forage crop growth monitoring and find the development direction, first, methods of UAV crop remote sensing monitoring were briefly described from two aspects of data acquisition and processing. Second, three key technologies of forage crop including canopy information extraction, spectral feature optimization and forage biomass estimation were described. Then the development trend of related research in recent years was analyzed, and it was pointed out that the number of papers published on UAV remote sensing forage crop monitoring showed an overall trend of rapidly increasing. With the rapid development of computer information technology and remote sensing technology, the application potential of UAV in the field of forage crop monitoring has been fully explored. Then, the research progress of UAV remote sensing in forage crop growth monitoring was described in five parts according to sensor types, i.e., visible, multispectral, hyperspectral, thermal infrared and LiDAR, and the research of each type of sensor were summarized and reviewed, pointing out that the current researches of hyperspectral, thermal infrared and LiDAR sensors in forage crop monitoring were less than that of visible and multispectral sensors. Finally, the future development directions were clarified according to the key technical problems that have not been solved in the research and application of UAV remote sensing forage crop growth monitoring: (1) Build a multi-temporal growth monitoring model based on the characteristics of different growth stages and different growth years of forage crops, carry out UAV remote sensing monitoring of forage crops around representative research areas to further improve the scope of application of the model. (2) Establish a multi-source database of UAV remote sensing, and carry out integrated collaborative monitoring combined with satellite remote sensing data, historical yield, soil conductivity and other data. (3) Develop an intelligent and user-friendly UAV remote sensing data analysis system, and shorten the data processing time through 5G communication network and edge computing devices. This paper could provide relevant technical references and directional guidelines for researchers in the field of forage crops and further promote the application and development of precision agriculture technology. Reference | Related Articles | Metrics | Comments（0）

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Monitoring of Leaf Chlorophyll Content in Flue-Cured Tobacco Based on Hyperspectral Remote Sensing of Unmanned Aerial Vehicle LAI Jiazheng, LI Beibei, CHENG Xiang, SUN Feng, CHENG Juting, WANG Jing, ZHANG Qian, YE Xiefeng Smart Agriculture    2023, 5 (2): 68-81.   DOI: 10.12133/j.smartag.SA202303007 Abstract （495）   HTML （70）    PDF（pc） （3593KB）（2964）       Knowledge map    Save [Objective] Leaf chlorophyll content (LCC) of flue-cured Tobacco is an important indicator for characterizing the photosynthesis, nutritional status, and growth of the crop. Tobacco is an important economic crop with leaves as the main harvest object, it is crucial to monitor its LCC. Hyperspectral data can be used for the rapid estimation of LCC in flue-cured tobacco leaves, making it of great significance and application value. The purpose of this study was to efficiently and accurately estimate the LCC of flue-cured tobacco during different growth stages. [Methods] Zhongyan 100 was chose as the research object, five nitrogen fertilization levels were set. In each plot, three plants were randomly and destructively sampled, resulting in a total of 45 ground samples for each data collection. After transplanting, the reflectance data of the flue-cured tobacco canopy at six growth stages (32, 48, 61, 75, 89, and 109 d ) were collected using a UAV equipped with a Resonon Pika L hyperspectral. Spectral indices for the LCC estimation model of flue-cured tobacco were screened in two ways: (1) based on 18 published vegetation indices sensitive to LCC of crop leaves; (2) based on random combinations of any two bands in the wavelength range of 400‒1000 nm. The Difference Spectral Index (DSI), Ratio Spectral Index (RSI), and Normalized Spectral Index (NDSI) were calculated and plotted against LCC. The correlations between the three spectral indices and leaf LCC were calculated and plotted using contour maps. Five regression models, unary linear regression (ULR), multivariable linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR), were used to estimate the chlorophyll content. A regression estimate model of LCC based on various combinations of spectral indices was eventually constructed by comparing the prediction accuracies of single spectral index models multiple spectral index models at different growth stages. Results and Discussions] The results showed that the LCC range for six growth stages was 0.52‒2.95 mg/g. The standard deviation and coefficient of variation values demonstrated a high degree of dispersion in LCC, indicating differences in fertility between different treatments at the test site and ensuring the applicability of the estimation model within a certain range. Except for 109 d after transplanting, most vegetation indices were significantly correlated with LCC (p<0.01). Compared with traditional vegetation indices, the newly combined spectral indices significantly improved the correlation with LCC. The sensitive bands at each growth stage were relatively concentrated, and the spectral index combinations got high correlation with LCC were mainly distributed between 780‒940 nm and 520‒710 nm. The sensitive bands for the whole growth stages were relatively dispersed, and there was little difference in the position of sensitive band between different spectral indices. For the univariate LCC estimation model, the highest modeling accuracy was achieved using the newly combined Normalized Spectral Index and Red Light Ratio Spectral Index at 75 d after transplanting. The coefficients of determination (R2 ) and root mean square errors (RMSE) for the modeling and validation sets were 0.822, 0.814, and 0.226, 0.230, respectively. The prediction results of the five resgression models showed that the RFR algorithm based on multivariate data performed best in LCC estimation. The R2 and RMSE of the modeling set using data at 75 d after transplanting were 0.891 and 0.205, while those of the validation set reached 0.919 and 0.146. In addition, the estimation performance of the univariate model based on the whole growth stages dataset was not ideal, with R2 of 0.636 and 0.686, and RMSE of 0.333 and 0.304 for the modeling and validation sets, respectively. However, the estimation accuracy of the model based on multiple spectral parameters was significantly improved in the whole growth stages dataset, with R2 of 0.854 and 0.802, and RMSE of 0.206 and 0.264 for the modeling and validation sets of the LCC-RFR model, respectively. In addition, in the whole growth stages dataset, the estimation accuracy of the LCC-RFR model was better than that of the LCC-MLR, LCC-PLSR, and LCC-SVR models. Compared with the modeling set, R2 increased by 19.06%, 18.62%, and 29.51%, while RMSE decreased by 31.93%, 29.51%, and 28.24%. Compared with the validation set, R2 increased by 8.21%, 12.62%, and 8.17%, while RMSE decreased by 3.76%, 9.33%, and 4.55%. [Conclusions] The sensitivity of vegetation indices (VIs) to LCC is closely connected to the tobacco growth stage, according to the results this study, which examined the reaction patterns of several spectral indices to LCC in flue-cured tobacco. The sensitivity of VIs to LCC at various growth stages is critical for crop parameter assessment using UAV hyperspectral photography. Five estimation models for LCC in flue-cured tobacco leaves were developed, with the LCC-RFR model demonstrating the greatest accuracy and stability. The RFR model is less prone to overfitting and can efficiently decrease outlier and noise interference. This work could provide theoretical and technological references for LCC estimate and flue-cured tobacco growth monitoring. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Real-time Detection Algorithm of Expanded Feed Image on the Water Surface Based on Improved YOLOv11 ZHOU Xiushan, WEN Luting, JIE Baifei, ZHENG Haifeng, WU Qiqi, LI Kene, LIANG Junneng, LI Yijian, WEN Jiayan, JIANG Linyuan Smart Agriculture    2024, 6 (6): 155-167.   DOI: 10.12133/j.smartag.SA202408014 Abstract （641）   HTML （77）    PDF（pc） （1858KB）（2662）       Knowledge map    Save [Objective] During the feeding process of fish populations in aquaculture, the video image characteristics of floating extruded feed on the water surface undergo continuous variations due to a myriad of environmental factors and fish behaviors. These variations pose significant challenges to the accurate detection of feed particles, which is crucial for effective feeding management. To address these challenges and enhance the detection of floating extruded feed particles on the water surface, ,thereby providing precise decision support for intelligent feeding in intensive aquaculture modes, the YOLOv11-AP2S model, an advanced detection model was proposed. [Methods] The YOLOv11-AP2S model enhanced the YOLOv11 algorithm by incorporating a series of improvements to its backbone network, neck, and head components. Specifically, an attention for fine-grained categorization (AFGC) mechanism was introduced after the 10th layer C2PSA of the backbone network. This mechanism aimed to boost the model's capability to capture fine-grained features, which were essential for accurately identifying feed particles in complex environments with low contrast and overlapping objects. Furthermore, the C3k2 module was replaced with the VoV-GSCSP module, which incorporated more sophisticated feature extraction and fusion mechanisms. This replacement further enhanced the network's ability to extract relevant features and improve detection accuracy. To improve the model's detection of small targets, a P2 layer was introduced. However, adding a P2 layer may increase computational complexity and resource consumption, so the overall performance and resource consumption of the model must be carefully balanced. To maintain the model's real-time performance while improving detection accuracy, a lightweight VoV-GSCSP module was utilized for feature fusion at the P2 layer. This approach enabled the YOLOv11-AP2S model to achieve high detection accuracy without sacrificing detection speed or model lightweights, making it suitable for real-time applications in aquaculture. [Results and Discussions] The ablation experimental results demonstrated the superiority of the YOLOv11-AP2S model over the original YOLOv11 network. Specifically, the YOLOv11-AP2S model achieved a precision ( P) and recall ( R) of 78.70%. The mean average precision (mAP50) at an intersection over union (IoU) threshold of 0.5 was as high as 80.00%, and the F1-Score had also reached 79.00%. These metrics represented significant improvements of 6.7%, 9.0%, 9.4% (for precision, as previously mentioned), and 8.0%, respectively, over the original YOLOv11 network. These improvements showed the effectiveness of the YOLOv11-AP2S model in detecting floating extruded feed particles in complex environments. When compared to other YOLO models, the YOLOv11-AP2S model exhibits clear advantages in detecting floating extruded feed images on a self-made dataset. Notably, under the same number of iterations, the YOLOv11-AP2S model achieved higher mAP50 values and lower losses, demonstrating its superiority in detection performance. This indicated that the YOLOv11-AP2S model strikes a good balance between learning speed and network performance, enabling it to efficiently and accurately detect images of floating extruded feed on the water surface. Furthermore, the YOLOv11-AP2S model's ability to handle complex detection scenarios, such as overlapping and adhesion of feed particles and occlusion by bubbles, was noteworthy. These capabilities were crucial for accurate detection in practical aquaculture environments, where such challenges were common and can significantly impair the performance of traditional detection systems. The improvements in detection accuracy and efficiency made the YOLOv11-AP2S model a valuable tool for intelligent feeding systems in aquaculture, as it could provide more reliable and timely information on fish feeding behavior. Additionally, the introduction of the P2 layer and the use of the lightweight VoV-GSCSP module for feature fusion at this layer contributed to the model's overall performance. These enhancements enabled the model to maintain high detection accuracy while keeping computational costs and resource consumption within manageable limits. This was particularly important for real-time applications in aquaculture, where both accuracy and efficiency were critical for effective feeding management. [Conclusions] The successful application of the YOLOv11-AP2S model in detecting floating extruded feed particles demonstrates its potential to intelligent feeding systems in aquaculture. By providing accurate and timely information on fish feeding behavior, the model can help optimize feeding strategies, reduce feed waste, and improve the overall efficiency and profitability of aquaculture operations. Furthermore, the model's ability to handle complex detection scenarios and maintain high detection accuracy while keeping computational costs within manageable limits makes it a practical and valuable tool for real-time applications in aquaculture. Therefore, the YOLOv11-AP2S model holds promise for wide application in intelligent aquaculture management, contributing to the sustainability and growth of the aquaculture industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress of Apple Production Intelligent Chassis and Weeding and Harvesting Equipment Technology DUAN Luojia, YANG Fuzeng, YAN Bin, SHI shuaiqi, QIN jifeng Smart Agriculture    2022, 4 (3): 24-41.   DOI: 10.12133/j.smartag.SA202206010 Abstract （689）   HTML （56）    PDF（pc） （1521KB）（2645）       Knowledge map    Save As a pillar industry of economic development in the main apple-producing areas, apple industry has made important contributions to the increase of local farmers' income. With the transformation and upgrading of apple industry, the mechanization and intelligence level would be directly related to economic benefits. To promote the research of apple production intelligent technology and the development of intelligent equipment, in this paper, the current level of mechanization in each step of apple production was first introduced. Then, the main characteristics of the main apple orchard machinery, such as power chassis, weeding machinery, and harvesting equipment, were demonstrated. The application progress of automatic leveling and control, automatic navigation, automatic obstacle avoidance, weed identification, weed removal, apple identification, apple positioning, apple separation, and other technologies in intelligent power chassis, intelligent weeding machines, and apple harvesting robots, were summarized. The basic principles and characteristics of the above three key technologies of intelligent equipment were expounded in combination with different application environments. Intelligent control is the key technology for the intelligentization of orchard power chassis. The post of chassis adaptive control technology and autonomous navigation technology were discussed. In addition, a chassis intelligent perception and intelligent decision-making system should be established. Orchard chassis safe, accurate, efficient, and stable driving and operation is the future development trend of orchard intelligent chassis. The lack of robust weed sensing technology is the main limitation to the commercial development of a robotic weed control system. To improve the level of weed detection and weeding, machine vision and multi-sensor fusion methods have been proposed to solve the practical problems, such as illumination, overlapping leaves, occlusion, and classifier or network structure optimization. Robotic apple harvesting has proven to be a highly challenging task due to environmental complexities, sensor reliability, and robot stability. To improve the accuracy and efficiency of harvest mechanization applications in apples, apple quick identification under complex scenes, apple picking path planning, and materials and structure of manipulator for apple picking must all be optimized accordingly. Finally, the challenges of intelligent equipment technologies in apple production were analyzed, and the developing suggestions were put forward. This research can provide references and ideas for the advancement of intelligent technology research in apple production and the research and development of intelligent equipment. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan Smart Agriculture    2024, 6 (6): 1-22.   DOI: 10.12133/j.smartag.SA202410018 Abstract （989）   HTML （163）    PDF（pc） （3047KB）（2626）       Knowledge map    Save [Significance] Rice smart unmanned farm is the core component of smart agriculture, and it is a key path to realize the modernization of rice production and promote the high-quality development of agriculture. Leveraging advanced information technologies such as the Internet of Things (IoT) and artificial intelligence (AI), these farms enable deep integration of data-driven decision making and intelligent machines. This integration creates an unmanned production system that covers the entire process from planting and managing rice crops to harvesting, greatly improving the efficiency and precision of rice cultivation. [Progress] This paper systematically sorted out the key technologies of rice smart unmanned farms in the three main links of pre-production, production and post-production, and the key technologies of pre-production mainly include the construction of high-standard farmland, unmanned nursery, land leveling, and soil nutrient testing. The construction of high-standard farmland is the foundation of the physical environment of the smart unmanned farms of rice, which provides perfect operating environment for the operation of modernized smart farm machinery through the reasonable layout of the field roads, good drainage and irrigation systems, and the scientific planting structure. Agricultural machine operation provides a perfect operating environment. The technical level of unmanned nursery directly determines the quality of rice cultivation and harvesting in the later stage, and a variety of rice seeding machines and nursery plate setting machines have been put into use. Land leveling technology can improve the growing environment of rice and increase the land utilization rate, and the current land leveling technology through digital sensing and path planning technology, which improves the operational efficiency and reduces the production cost at the same time. Soil nutrient detection technology is mainly detected by electrochemical analysis and spectral analysis, but both methods have their advantages and disadvantages, how to integrate the two methods to achieve an all-round detection of soil nutrient content is the main direction of future research. The key technologies in production mainly include rice dry direct seeding, automated transplanting, precise variable fertilization, intelligent irrigation, field weed management, and disease diagnosis. Among them, the rice dry direct seeding technology requires the planter to have high precision and stability to ensure reasonable seeding depth and density. Automated rice transplanting technology mainly includes three ways: root washing seedling machine transplanting, blanket seedling machine transplanting, and potting blanket seedling machine transplanting; at present, the incidence of problems in the automated transplanting process should be further reduced, and the quality and efficiency of rice machine transplanting should be improved. Precision variable fertilization technology is mainly composed of three key technologies: information perception, prescription decision-making and precise operation, but there are still fewer cases of unmanned farms combining the three technologies, and in the future, the main research should be on the method of constructing the whole process operation system of variable fertilization. The smart irrigation system is based on the water demand of the whole life cycle of rice to realize adaptive irrigation control, and the current smart irrigation technology can automatically adjust the irrigation strategy through real-time monitoring of soil, climate and crop growth conditions to further improve irrigation efficiency and agricultural production benefits. The field weed management and disease diagnosis technology mainly recognizes rice weeds as well as diseases through deep learning and other methods, and combines them with precision application technology for prevention and intervention. Post-production key technologies mainly include rice yield estimation, unmanned harvesting, rice storage and processing quality testing. Rice yield estimation technology is mainly used to predict yield by combining multi-source data and algorithms, but there are still problems such as the difficulty of integrating multi-source data, which requires further research. In terms of unmanned aircraft harvesting technology, China's rice combine harvester market has tended to stabilize, and the safety of the harvester's autopilot should be further improved in the future. Rice storage and processing quality detection technology mainly utilizes spectral technology and machine vision technology to detect spectra and images, and future research can combine deep learning and multimodal fusion technology to improve the machine vision system's ability and adaptability to recognize the appearance characteristics of rice. [Conclusions and Prospects] This paper reviews the researches of the construction of intelligent unmanned rice farms at home and abroad in recent years, summarizes the main difficulties faced by the key technologies of unmanned farms in practical applications, analyzes the challenges encountered in the construction of smart unmanned farms, summarizes the roles and responsibilities of the government, enterprises, scientific research institutions, cooperatives and other subjects in promoting the construction of intelligent unmanned rice farms, and puts forward relevant suggestions. It provides certain support and development ideas for the construction of intelligent unmanned rice farms in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Digital Twin for Agricultural Machinery: From Concept to Application GUO Dafang, DU Yuefeng, WU Xiuheng, HOU Siyu, LI Xiaoyu, ZHANG Yan'an, CHEN Du Smart Agriculture    2023, 5 (2): 149-160.   DOI: 10.12133/j.smartag.SA202305007 Abstract （1257）   HTML （179）    PDF（pc） （2531KB）（2584）       Knowledge map    Save Significance Agricultural machinery serves as the fundamental support for implementing advanced agricultural production concepts. The key challenge for the future development of smart agriculture lies in how to enhance the design, manufacturing, operation, and maintenance of these machines to fully leverage their capabilities. To address this, the concept of the digital twin has emerged as an innovative approach that integrates various information technologies and facilitates the integration of virtual and real-world interactions. By providing a deeper understanding of agricultural machinery and its operational processes, the digital twin offers solutions to the complexity encountered throughout the entire lifecycle, from design to recycling. Consequently, it contributes to an all-encompassing enhancement of the quality of agricultural machinery operations, enabling them to better meet the demands of agricultural production. Nevertheless, despite its significant potential, the adoption of the digital twin for agricultural machinery is still at an early stage, lacking the necessary theoretical guidance and methodological frameworks to inform its practical implementation. Progress Drawing upon the successful experiences of the author's team in the digital twin for agricultural machinery, this paper presents an overview of the research progress made in digital twin. It covers three main areas: The digital twin in a general sense, the digital twin in agriculture, and the digital twin for agricultural machinery. The digital twin is conceptualized as an abstract notion that combines model-based system engineering and cyber-physical systems, facilitating the integration of virtual and real-world environments. This paper elucidates the relevant concepts and implications of digital twin in the context of agricultural machinery. It points out that the digital twin for agricultural machinery aims to leverage advanced information technology to create virtual models that accurately describe agricultural machinery and its operational processes. These virtual models act as a carrier, driven by data, to facilitate interaction and integration between physical agricultural machinery and their digital counterparts, consequently yielding enhanced value. Additionally, it proposes a comprehensive framework comprising five key components: Physical entities, virtual models, data and connectivity, system services, and business applications. Each component's functions operational mechanism, and organizational structure are elucidated. The development of the digital twin for agricultural machinery is still in its conceptual phase, and it will require substantial time and effort to gradually enhance its capabilities. In order to advance further research and application of the digital twin in this domain, this paper integrates relevant theories and practical experiences to propose an implementation plan for the digital twin for agricultural machinery. The macroscopic development process encompasses three stages: Theoretical exploration, practical application, and summarization. The specific implementation process entails four key steps: Intelligent upgrading of agricultural machinery, establishment of information exchange channels, construction of virtual models, and development of digital twin business applications. The implementation of digital twin for agricultural machinery comprises four stages: Pre-research, planning, implementation, and evaluation. The digital twin serves as a crucial link and bridge between agricultural machinery and the smart agriculture. It not only facilitates the design and manufacturing of agricultural machinery, aligning them with the realities of agricultural production and supporting the advancement of advanced manufacturing capabilities, but also enhances the operation, maintenance, and management of agricultural production to better meet practical requirements. This, in turn, expedites the practical implementation of smart agriculture. To fully showcase the value of the digital twin for agricultural machinery, this paper addresses the existing challenges in the design, manufacturing, operation, and management of agricultural machinery. It expounds the methods by which the digital twin can address these challenges and provides a technical roadmap for empowering the design, manufacturing, operation, and management of agricultural machinery through the use of the digital twin. In tackling the critical issue of leveraging the digital twin to enhance the operational quality of agricultural machinery, this paper presents two research cases focusing on high-powered tractors and large combine harvesters. These cases validate the feasibility of the digital twin in improving the quality of plowing operations for high-powered tractors and the quality of grain harvesting for large combine harvesters. Conclusions and Prospects This paper serves as a reference for the development of research on digital twin for agricultural machinery, laying a theoretical foundation for empowering smart agriculture and intelligent equipment with the digital twin. The digital twin provides a new approach for the transformation and upgrade of agricultural machinery, offering a new path for enhancing the level of agricultural mechanization and presenting new ideas for realizing smart agriculture. However, existing digital twin for agricultural machinery is still in its early stages, and there are a series of issues that need to be explored. It is necessary to involve more professionals from relevant fields to advance the research in this area. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Evaluation System of China's Low-Carbon Cold Chain Logistics Development Level YANG Bin, HAN Jiawei, YANG Lin, REN Qingshan, YANG Xinting Smart Agriculture    2023, 5 (1): 44-51.   DOI: 10.12133/j.smartag.SA202301011 Abstract （694）   HTML （77）    PDF（pc） （707KB）（2553）       Knowledge map    Save In recent years, China's cold chain logistics industry has entered a stage of rapid development. At the same time, with the increase of greenhouse gas emissions, green and low-carbon transformation has become a new feature and direction of high-quality and healthy development of the cold chain industry to meet the future development needs of China's low-carbon economy. In view of this, in order to ensure the scientificity of China's low-carbon cold chain logistics evaluation system, in this paper, 30 indicators from the four levels of energy transformation, technological innovation, economic efficiency, and national policy based on different relevant levels were first preliminarily determined, and finally 14 indicators for building China's low-carbon cold chain logistics development evaluation system through consulting experts and the possibility of data acquisition were determined. Data from 2017 to 2021 were selected to conduct a quantitative evaluation of the development level of low-carbon cold chain logistics in China. Firstly, the entropy weight method was used to analyze the weight and obstacle degree of different indicators to explore the impact of different indicators on the development of low-carbon cold chain logistics; Secondly, a weighted decision-making matrix was constructed based on the weights of different indicators, and the technology for order preference by similarity to ideal solution (TOPSIS) evaluation model was used to evaluate the development of low-carbon cold chain logistics in China from 2017 to 2021, in order to determine the development and changes of low-carbon cold chain logistics in China. The research results showed that among the 14 different indicators of the established evaluation system for the development of low-carbon cold chain logistics in China, the growth rate of the use of green packaging materials, the number of low-carbon technical papers published, the proportion of scientific research personnel, the growth rate of cold chain logistics demand for fresh agricultural products, and the reduction rate of hydrochlorofluorocarbon refrigerants account for a relatively large proportion, ranking in the top five, respectively reaching 0.1243, 0.1074, 0.1066, 0.0982, and 0.0716, accounting for more than half of the overall proportion. It has a significant impact on the development of low-carbon cold chain logistics in China. From 2017 to 2021, the development level of China's low-carbon cold chain logistics was scored from 0.1498 to 0.2359, with a year-on-year increase of about 57.5%, indicating that China's low-carbon cold chain logistics development level was relatively fast in the past five years. Although China's low-carbon cold chain logistics development has shown an overall upward trend, it is still in the development stage. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Path of Smart Agricultural Technology Innovation Leading Development of Agricultural New Quality Productivity CAO Bingxue, LI Hongfei, ZHAO Chunjiang, LI Jin Smart Agriculture    2024, 6 (4): 116-127.   DOI: 10.12133/j.smartag.SA202405004 Abstract （1322）   HTML （224）    PDF（pc） （1102KB）（2532）       Knowledge map    Save [Significance] Building the agricultural new quality productivity is of great significance. It is the advanced quality productivity which realizes the transformation, upgrading, and deep integration of substantive, penetrating, operational, and media factors, and has outstanding characteristics such as intelligence, greenness, integration, and organization. As a new technology revolution in the field of agriculture, smart agricultural technology transforms agricultural production mode by integrating agricultural biotechnology, agricultural information technology, and smart agricultural machinery and equipment, with information and knowledge as important core elements. The inherent characteristics of "high-tech, high-efficiency, high-quality, and sustainable" in agricultural new quality productivity are fully reflected in the practice of smart agricultural technology innovation. And it has become an important core and engine for promoting the agricultural new quality productivity. [Progress] Through literature review and theoretical analysis, this article conducts a systematic study on the practical foundation, internal logic, and problem challenges of smart agricultural technology innovation leading the development of agricultural new quality productivity. The conclusions show that: (1) At present, the global innovation capability of smart agriculture technology is constantly enhancing, and significant technology breakthroughs have been made in fields such as smart breeding, agricultural information perception, agricultural big data and artificial intelligence, smart agricultural machinery and equipment, providing practical foundation support for leading the development of agricultural new quality productivity. Among them, the smart breeding of 'Phenotype+Genotype+Environmental type' has entered the fast lane, the technology system for sensing agricultural sky, air, and land information is gradually maturing, the research and exploration on agricultural big data and intelligent decision-making technology continue to advance, and the creation of smart agricultural machinery and equipment for different fields has achieved fruitful results; (2) Smart agricultural technology innovation provides basic resources for the development of agricultural new quality productivity through empowering agricultural factor innovation, provides sustainable driving force for the development of agricultural new quality productivity through empowering agricultural technology innovation, provides practical paradigms for the development of agricultural new quality productivity through empowering agricultural scenario innovation, provides intellectual support for the development of agricultural new quality productivity through empowering agricultural entity innovation, and provides important guidelines for the development of agricultural new quality productivity through empowering agricultural value innovation; (3) Compared to the development requirements of agricultural new quality productivity in China and the advanced level of international smart agriculture technology, China's smart agriculture technology innovation is generally in the initial stage of multi-point breakthroughs, system integration, and commercial application. It still faces major challenges such as an incomplete policy system for technology innovation, key technologies with bottlenecks, blockages and breakpoints, difficulties in the transformation and implementation of technology achievements, and incomplete support systems for technology innovation. [Conclusions and Prospects] Regarding the issue of technology innovation in smart agriculture, this article proposes the 'Four Highs' path of smart agriculture technology innovation to fill the gaps in smart agriculture technology innovation and accelerate the formation of agricultural new quality productivity in China. The "Four Highs" path specifically includes the construction of high-energy smart agricultural technology innovation platforms, the breakthroughs in high-precision and cutting-edge smart agricultural technology products, the creation of high-level smart agricultural application scenarios, and the cultivation of high-level smart agricultural innovation talents. Finally, this article proposes four strategic suggestions such as deepening the understanding of smart agriculture technology innovation and agricultural new quality productivity, optimizing the supply of smart agriculture technology innovation policies, building a national smart agriculture innovation development pilot zone, and improving the smart agriculture technology innovation ecosystem. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Lightweight Daylily Grading and Detection Model Based on Improved YOLOv10 JIN Xuemeng, LIANG Xiyin, DENG Pengfei Smart Agriculture    2024, 6 (5): 108-118.   DOI: 10.12133/j.smartag.SA202407022 Abstract （507）   HTML （68）    PDF（pc） （1532KB）（2498）       Knowledge map    Save [Objective] In the agricultural production, accurately classifying dried daylily grades is a critical task with significant economic implications. However, current target detection models face challenges such as inadequate accuracy and excessive parameters when applied to dried daylily grading, limiting their practical application and widespread use in real-world settings. To address these issues, an innovative lightweight YOLOv10-AD network model was proposed. The model aims to enhance detection accuracy by optimizing the network structure and loss functions while reducing parameters and computational costs, making it more suitable for deployment in resource-constrained agricultural production environments. [Methods] The dried daylilies selected from the Qingyang region of Gansu province as the research subject. A large number of images of dried daylilies, categorized into three grades superior, medium, and inferior, were collected using mobile phones under varying lighting conditions and backgrounds. The images were carefully annotated and augmented to build a comprehensive dataset for dried daylily grade classification. YOLOv10 was chosen as the base network, and a newly designed backbone network called AKVanillaNet was introduced. AKVanillaNet combines AKConv (adaptive kernel convolution) with VanillaNet's deep learning and shallow inference mechanisms. The second convolutional layer in VanillaNet was replaced with AKConv, and AKConv was merged with standard convolution layers at the end of the training phase to optimize the model for capturing the unique shape characteristics of dried daylilies. This innovative design not only improved detection accuracy but also significantly reduced the number of parameters and computational costs. Additionally, the DysnakeConv module was integrated into the C2f structure, replacing the Bottleneck layer with a Bottleneck-DS layer to form the new C2f-DysnakeConv module. This module enhanced the model's sensitivity to the shapes and boundaries of targets, allowing the neural network to better capture the shape information of irregular objects like dried daylilies, further improving the model's feature extraction capability. The Powerful-IOU (PIOU) loss function was also employed, which introduced a target-size-adaptive penalty factor and a gradient adjustment function. This design guided the anchor box regression along a more direct path, helping the model better fit the data and improve overall performance. [Results and Discussions] The testing results on the dried daylily grade classification dataset demonstrated that the YOLOv10-AD model achieved a mean average precision (mAP) of 85.7%. The model's parameters, computational volume, and size were 2.45 M, 6.2 GFLOPs, and 5.0 M, respectively, with a frame rate of 156 FPS. Compared to the benchmark model, YOLOv10-AD improved mAP by 5.7% and FPS by 25.8%, while reducing the number of parameters, computational volume, and model size by 9.3%, 24.4%, and 9.1%, respectively. These results indicated that YOLOv10-AD not only improved detection accuracy but also reduced the model's complexity, making it easier to deploy in real-world production environments. Furthermore, YOLOv10-AD outperformed larger models in the same series, such as YOLOv10s and YOLOv10m. Specifically, the weight, parameters, and computational volume of YOLOv10-AD were only 31.6%, 30.5%, and 25.3% of those in YOLOv10s, and 15.7%, 14.8%, and 9.8% of YOLOv10m. Despite using fewer resources, YOLOv10-AD achieved a mAP increase of 2.4% over YOLOv10s and 1.9% over YOLOv10m. These findings confirm that YOLOv10-AD maintains high detection accuracy while requiring significantly fewer resources, making it more suitable for agricultural production environments where computational capacity may be limited. The study also examined the performance of YOLOv10-AD under different lighting conditions. The results showed that YOLOv10-AD achieved an average accuracy of 92.3% in brighter environments and 78.6% in darker environments. In comparison, the YOLOv10n model achieved 88.9% and 71.0% in the same conditions, representing improvements of 3.4% and 7.6%, respectively. These findings demonstrate that YOLOv10-AD has a distinct advantage in maintaining high accuracy and confidence in grading dried daylilies across varying lighting conditions. [Conclusions] The YOLOv10-AD network model proposed significantly reduces the number of parameters and computational costs without compromising detection accuracy. This model presents a valuable technical reference for intelligent classification of dried daylily grades in agricultural production environments, particularly where resources are constrained. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Advances, Problems and Challenges of Precise Estrus Perception and Intelligent Identification Technology for Cows ZHANG Zhiyong, CAO Shanshan, KONG Fantao, LIU Jifang, SUN Wei Smart Agriculture    DOI: 10.12133/j.smartag.SA202305005 Online available: 08 January 2025

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Wheat Lodging Types Detection Based on UAV Image Using Improved EfficientNetV2 LONG Jianing, ZHANG Zhao, LIU Xiaohang, LI Yunxia, RUI Zhaoyu, YU Jiangfan, ZHANG Man, FLORES Paulo, HAN Zhexiong, HU Can, WANG Xufeng Smart Agriculture    2023, 5 (3): 62-74.   DOI: 10.12133/j.smartag.SA202308010 Abstract （598）   HTML （52）    PDF（pc） （2022KB）（2447）       Knowledge map    Save [Objective] Wheat, as one of the major global food crops, plays a key role in food production and food supply. Different influencing factors can lead to different types of wheat lodging, e.g., root lodging may be due to improper use of fertilizers. While stem lodging is mostly due to harsh environments, different types of wheat lodging can have different impacts on yield and quality. The aim of this study was to categorize the types of wheat lodging by unmanned aerial vehicle (UAV) image detection and to investigate the effect of UAV flight altitude on the classification performance. [Methods] Three UAV flight altitudes (15, 45, and 91 m) were set to acquire images of wheat test fields. The main research methods contained three parts: an automatic segmentation algorithm, wheat classification model selection, and an improved classification model based on EfficientNetV2-C. In the first part, the automatic segmentation algorithm was used to segment the UAV to acquire the wheat test field at three different heights and made it into the training dataset needed for the classification model. The main steps were first to preprocess the original wheat test field images acquired by the UAV through scaling, skew correction, and other methods to save computation time and improve segmentation accuracy. Subsequently, the pre-processed image information was analyzed, and the green part of the image was extracted using the super green algorithm, which was binarized and combined with the edge contour extraction algorithm to remove the redundant part of the image to extract the region of interest, so that the image was segmented for the first time. Finally, the idea of accumulating pixels to find sudden value added was used to find the segmentation coordinates of two different sizes of wheat test field in the image, and the region of interest of the wheat test field was segmented into a long rectangle and a short rectangle test field twice, so as to obtain the structural parameters of different sizes of wheat test field and then to generate the dataset of different heights. In the second part, four machine learning classification models of support vector machine (SVM), K nearest neighbor (KNN), decision tree (DT), and naive bayes (NB), and two deep learning classification models (ResNet101 and EfficientNetV2) were selected. Under the unimproved condition, six classification models were utilized to classify the images collected from three UAVs at different flight altitudes, respectively, and the optimal classification model was selected for improvement. In the third part, an improved model, EfficientNetV2-C, with EfficientNetV2 as the base model, was proposed to classify and recognized the lodging type of wheat in test field images. The main improvement points were attention mechanism improvement and loss function improvement. The attention mechanism was to replace the original model squeeze and excitation (SE) with coordinate attention (CA), which was able to embed the position information into the channel attention, aggregate the features along the width and height directions, respectively, during feature extraction, and capture the long-distance correlation in the width direction while retaining the long-distance correlation in the length direction, accurate location information, enhancing the feature extraction capability of the network in space. The loss function was replaced by class-balanced focal loss (CB-Focal Loss), which could assign different loss weights according to the number of valid samples in each class when targeting unbalanced datasets, effectively solving the impact of data imbalance on the classification accuracy of the model. [Results and Discussions] Four machine learning classification results: SVM average classification accuracy was 81.95%, DT average classification accuracy was 79.56%, KNN average classification accuracy was 59.32%, and NB average classification accuracy was 59.48%. The average classification accuracy of the two deep learning models, ResNet101 and EfficientNetV2, was 78.04%, and the average classification accuracy of ResNet101 was 81.61%. Comparing the above six classification models, the EfficientNetV2 classification model performed optimally at all heights. And the improved EfficientNetV2-C had an average accuracy of 90.59%, which was 8.98% higher compared to the average accuracy of EfficientNetV2. The SVM classification accuracies of UAVs at three flight altitudes of 15, 45, and 91 m were 81.33%, 83.57%, and 81.00%, respectively, in which the accuracy was the highest when the altitude was 45 m, and the classification results of the SVM model values were similar to each other, which indicated that the imbalance of the input data categories would not affect the model's classification effect, and the SVM classification model was able to solve the problem of high dimensionality of the data efficiently and had a good performance for small and medium-sized data sets. The SVM classification model could effectively solve the problem of the high dimensionality of data and had a better classification effect on small and medium-sized datasets. For the deep learning classification model, however, as the flight altitude increases from 15 to 91 m, the classification performance of the deep learning model decreased due to the loss of image feature information. Among them, the classification accuracy of ResNet101 decreased from 81.57% to 78.04%, the classification accuracy of EfficientNetV2 decreased from 84.40% to 81.61%, and the classification accuracy of EfficientNetV2-C decreased from 97.65% to 90.59%. The classification accuracy of EfficientNetV2-C at each of the three altitudes. The difference between the values of precision, recall, and F1-Score results of classification was small, which indicated that the improved model in this study could effectively solve the problems of unbalanced model classification results and poor classification effect caused by data imbalance. [Conclusions] The improved EfficientNetV2-C achieved high accuracy in wheat lodging type detection, which provides a new solution for wheat lodging early warning and crop management and is of great significance for improving wheat production efficiency and sustainable agricultural development. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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GRA-WHO-TCN Combination Model for Forecasting Cold Chain Logistics Demand of Agricultural Products LIU Yan, JI Juncheng Smart Agriculture    2024, 6 (3): 148-158.   DOI: 10.12133/j.smartag.SA202310006 Abstract （521）   HTML （39）    PDF（pc） （1280KB）（2409）       Knowledge map    Save [Objective] As a critical component of agricultural product supply chain management, cold chain logistics demand prediction encounters challenges such as inadequate feature extraction, high nonlinearity of data, and the propensity for algorithms to become trapped in local optima during the digital transformation process. To address these issues and enhance the accuracy of demand prediction, achieve intelligent management of the agricultural product supply chain, a combined forecasting model that integrates grey relational analysis (GRA), the wild horse optimizer (WHO), and temporal convolutional networks (TCN) is proposed in this research. [Methods] Firstly, a cold chain logistics indicator system was established for the data of Zhejiang province, China, spanning the years 2000 to 2020. This system covered four key aspects: the economic scale of agricultural products, logistics transportation, digital technology, and agricultural product supply. Then, the GRA was applied to identify relevant indicators of cold chain logistics for agricultural products in Zhejiang province, with 17 indicators selected that had a correlation degree higher than 0.75. Sliding window technology, a problem-solving approach for data structures and algorithms, suitable for reducing the time complexity of data to a better level and improving the execution efficiency of algorithms, was used to partition the selected indicators. Secondly, the TCN model was employed to extract features of different scales by stacking multiple convolutional layers. Each layer utilized different-sized convolutional kernels to capture features within different time ranges. By utilizing the dilated convolutional module of TCN, temporal and spatial relationships within economic data were effectively mined, considering the temporal characteristics of socio-economic data and logistics information in the agricultural supply chain, and exploring the temporal and spatial features of economic data. Simultaneously, the WHO algorithm was applied to optimize five hyperparameters of the TCN model, including the number of TCN layers, the number of filters, residual blocks, Dense layers, and neurons within the Dense layer. Finally, the optimized GRA-WHO-TCN model was used to extract and analyze features from highly nonlinear multidimensional economic data, ultimately facilitating the prediction of cold chain logistics demand. [Results and Discussions] For comparative analysis of the superiority of the GRA-WHO-TCN model, the 17 selected indicators were input into long short-term memory (LSTM), TCN, WHO-LSTM, and WHO-TCN models. The parameters optimized by the WHO algorithm for the TCN model were set respectively: 2 TCN layer was, 2 residual blocks, 1 dense layer, 60 filters, and 16 neurons in the dense layer. The optimized GRA-WHO-TCN temporal model can effectively extract the temporal and spatial features of multidimensional data, fully explore the implicit relationships among indicator factors, and demonstrating good fitting effects. Compared to GRA-LSTM and GRA-TCN models, the GRA-TCN model exhibited superior performance, with a lower root mean square error of 37.34 and a higher correlation coefficient of 0.91, indicating the advantage of the TCN temporal model in handling complex nonlinear data. Furthermore, the GRA-WHO-LSTM and GRA-WHO-TCN models optimized by the WHO algorithm had improved prediction accuracy and stability compared to GRA-LSTM and GRA-TCN models, illustrating that the WHO algorithm effectively optimized model parameters to enhance the effectiveness of model fitting. When compared to the GRA-WHO-LSTM model, the GRA-WHO-TCN model displayed a lower root mean square error of 11.3 and an effective correlation coefficient of 0.95, predicting cold chain logistics demand quantities in Zhejiang province for the years 2016-2020 as 29.8, 30.46, 24.87, 26.45, and 27.99 million tons, with relative errors within 0.6%, achieving a high level of prediction accuracy. This achievement showcases a high level of prediction accuracy and underscores the utility of the GRA-WHO-TCN model in forecasting complex data scenarios. [Conclusions] The proposed GRA-WHO-TCN model demonstrated superior parameter optimization capabilities and predictive accuracy compared to the GRA-LSTM and GRA-TCN models. The predicted results align well with the development of cold chain logistics of agricultural products in Zhejiang province. This provides a scientific prediction foundation and practical reference value for the development of material flow and information flow in the agricultural supply chain under the digital economy context. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi Smart Agriculture    2024, 6 (5): 128-138.   DOI: 10.12133/j.smartag.SA202406012 Abstract （658）   HTML （97）    PDF（pc） （1702KB）（2246）       Knowledge map    Save [Objective] As one of China's most important agricultural products, apples hold a significant position in cultivation area and yield. However, during the growth process, apples are prone to various diseases that not only affect the quality of the fruit but also significantly reduce the yield, impacting farmers' economic benefits and the stability of market supply. To reduce the incidence of apple diseases and increase fruit yield, developing efficient and fast apple leaf disease detection technology is of great significance. An improved YOLOv8 algorithm was proposed to identify the leaf diseases that occurred during the growth of apples. [Methods] YOLOv8n model was selected to detect various leaf diseases such as brown rot, rust, apple scab, and sooty blotch that apples might encounter during growth. SPD-Conv was introduced to replace the original convolutional layers to retain fine-grained information and reduce model parameters and computational costs, thereby improving the accuracy of disease detection. The multi-scale dilated attention (MSDA) attention mechanism was added at appropriate positions in the Neck layer to enhance the model's feature representation capability, which allowed the model to learn the receptive field dynamically and adaptively focus on the most representative regions and features in the image, thereby enhancing the ability to extract disease-related features. Finally, inspired by the RepVGG architecture, the original detection head was optimized to achieve a separation of detection and inference architecture, which not only accelerated the model's inference speed but also enhanced feature learning capability. Additionally, a dataset of apple leaf diseases containing the aforementioned diseases was constructed, and experiments were conducted. [Results and Discussions] Compared to the original model, the improved model showed significant improvements in various performance metrics. The mAP50 and mAP50:95 achieved 88.2% and 37.0% respectively, which were 2.7% and 1.3% higher than the original model. In terms of precision and recall, the improved model increased to 83.1% and 80.2%, respectively, representing an improvement of 0.9% and 1.1% over the original model. Additionally, the size of the improved model was only 7.8 MB, and the computational cost was reduced by 0.1 G FLOPs. The impact of the MSDA placement on model performance was analyzed by adding it at different positions in the Neck layer, and relevant experiments were designed to verify this. The experimental results showed that adding MSDA at the small target layer in the Neck layer achieved the best effect, not only improving model performance but also maintaining low computational cost and model size, providing important references for the optimization of the MSDA mechanism. To further verify the effectiveness of the improved model, various mainstream models such as YOLOv7-tiny, YOLOv9-c, RetinaNet, and Faster-RCNN were compared with the propoed model. The experimental results showed that the improved model outperformed these models by 1.4%, 1.3%, 7.8%, and 11.6% in mAP50, 2.8%, 0.2%, 3.4%, and 5.6% in mAP50:95. Moreover, the improved model showed significant advantages in terms of floating-point operations, model size, and parameter count, with a parameter count of only 3.7 MB, making it more suitable for deployment on hardware-constrained devices such as drones. In addition, to assess the model's generalization ability, a stratified sampling method was used, selecting 20% of the images from the dataset as the test set. The results showed that the improved model could maintain a high detection accuracy in complex and variable scenes, with mAP50 and mAP50:95 increasing by 1.7% and 1.2%, respectively, compared to the original model. Considering the differences in the number of samples for each disease in the dataset, a class balance experiment was also designed. Synthetic samples were generated using oversampling techniques to increase the number of minority-class samples. The experimental results showed that the class-balanced dataset significantly improved the model's detection performance, with overall accuracy increasing from 83.1% to 85.8%, recall from 80.2% to 83.6%, mAP50 from 88.2% to 88.9%, and mAP50:95 from 37.0% to 39.4%. The class-balanced dataset significantly enhanced the model's performance in detecting minority diseases, thereby improving the overall performance of the model. [Conclusions] The improved model demonstrated significant advantages in apple leaf disease detection. By introducing SPD-Conv and MSDA attention mechanisms, the model achieved noticeable improvements in both precision and recall while effectively reducing computational costs, leading to more efficient detection capabilities. The improved model could provide continuous health monitoring throughout the apple growth process and offer robust data support for farmers' scientific decision-making before fruit harvesting. 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Path Tracking Control Algorithm of Tractor-Implement LIU Zhiyong, WEN Changkai, XIAO Yuejin, FU Weiqiang, WANG Hao, MENG Zhijun Smart Agriculture    2023, 5 (4): 58-67.   DOI: 10.12133/j.smartag.SA202308012 Abstract （400）   HTML （25）    PDF（pc） （1386KB）（2161）       Knowledge map    Save [Objective] The usual agricultural machinery navigation focuses on the tracking accuracy of the tractor, while the tracking effect of the trailed implement in the trailed agricultural vehicle is the core of the work quality. The connection mode of the tractor and the implement is non-rigid, and the implement can rotate around the hinge joint. In path tracking, this non-rigid structure, leads to the phenomenon of non-overlapping trajectories of the tractor and the implement, reduce the path tracking accuracy. In addition, problems such as large hysteresis and poor anti-interference ability are also very obvious. In order to solve the above problems, a tractor-implement path tracking control method based on variable structure sliding mode control was proposed, taking the tractor front wheel angle as the control variable and the trailed implement as the control target. [Methods] Firstly, the linear deviation model was established. Based on the structural relationship between the tractor and the trailed agricultural implements, the overall kinematics model of the vehicle was established by considering the four degrees of freedom of the vehicle: transverse, longitudinal, heading and articulation angle, ignoring the lateral force of the vehicle and the slip in the forward process. The geometric relationship between the vehicle and the reference path was integrated to establish the linear deviation model of vehicle-road based on the vehicle kinematic model and an approximate linearization method. Then, the control algorithm was designed. The switching function was designed considering three evaluation indexes: lateral deviation, course deviation and hinged angle deviation. The exponential reaching law was used as the reaching mode, the saturation function was used instead of the sign function to reduce the control variable jitter, and the convergence of the control law was verified by combining the Lyapunov function. The system was three-dimensional, in order to improve the dynamic response and steady-state characteristics of the system, the two conjugate dominant poles of the system were assigned within the required range, and the third point was kept away from the two dominant poles to reduce the interference on the system performance. The coefficient matrix of the switching function was solved based on the Ackermann formula, then the calculation formula of the tractor front wheel angle was obtained, and the whole control algorithm was designed. Finally, the path tracking control simulation experiment was carried out. The sliding mode controller was built in the MATLAB/Simulink environment, the controller was composed of the deviation calculation module and the control output calculation module. The tractor-implement model in Carsim software was selected with the front car as a tractor and the rear car as the single-axle implement, and tracking control simulation tests of different reference paths were conducted in the MATLAB/Carsim co-simulation environment. [Results and Discussions] Based on the co-simulation environment, the tracking simulation experiments of three reference paths were carried out. When tracking the double lane change path, the lateral deviation and heading deviation of the agricultural implement converged to 0 m and 0° after 8 s. When the reference heading changed, the lateral deviation and heading deviation were less than 0.1 m and less than 7°. When tracking the circular reference path, the lateral deviation of agricultural machinery tended to be stable after 7 s and was always less than 0.03 m, and the heading deviation of agricultural machinery tended to be stable after 7 s and remained at 0°. The simulation results of the double lane change path and the circular path showed that the controller could maintain good performance when tracking the constant curvature reference path. When tracking the reference path of the S-shaped curve, the tracking performance of the agricultural machinery on the section with constant curvature was the same as the previous two road conditions, and the maximum lateral deviation of the agricultural machinery at the curvature change was less than 0.05 m, the controller still maintained good tracking performance when tracking the variable curvature path. [Conclusions] The sliding mode variable structure controller designed in this study can effectively track the linear and circular reference paths, and still maintain a good tracking effect when tracking the variable curvature paths. Agricultural machinery can be on-line in a short time, which meets the requirements of speediness. In the tracking simulation test, the angle of the tractor front wheel and the articulated angle between the tractor and agricultural implement are kept in a small range, which meets the needs of actual production and reduces the possibility of safety accidents. In summary, the agricultural implement can effectively track the reference path and meet the requirements of precision, rapidity and safety. The model and method proposed in this study provide a reference for the automatic navigation of tractive agricultural implement. In future research, special attention will be paid to the tracking control effect of the control algorithm in the actual field operation and under the condition of large speed changes. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Status and Prospect of Quality Intelligent Control Technology in Facilities Environment of Characteristic Agricultural Products GUO Wei, WU Huarui, GUO Wang, GU Jingqiu, ZHU Huaji Smart Agriculture    2024, 6 (6): 44-62.   DOI: 10.12133/j.smartag.SA202411017 Abstract （511）   HTML （64）    PDF（pc） （3153KB）（2145）       Knowledge map    Save [Significance] In view of the lack of monitoring means of quality influence factors in the production process of characteristic agricultural products with in central and western regions of China, the weak ability of intelligent control, the unclear coupling relationship of quality control elements and the low degree of systematic application, the existing technologies described such as intelligent monitoring of facility environment, growth and nutrition intelligent control model, architecture of intelligent management and control platform and so on. Through the application of the Internet of Things, big data and the new generation of artificial intelligence technology, it provides technical support for the construction and application of intelligent process quality control system for the whole growth period of characteristic agricultural products. [Progress] The methods of environmental regulation and nutrition regulation are analyzed, including single parameters and combined control methods, such as light, temperature, humidity, CO2 concentration, fertilizer and water, etc. The multi-parameter coupling control method has the advantage of more comprehensive scene analysis. Based on the existing technology, a multi-factor coupling method of integrating growth state, agronomy, environment, input and agricultural work is put forward. This paper probes into the system architecture of the whole process service of quality control, the visual identification system of the growth process of agricultural products and the knowledge-driven agricultural technical service system, and introduces the technology of the team in the disease knowledge Q & A scene through multi-modal knowledge graph and large model technology. [Conclusions and Prospects] Based on the present situation of the production of characteristic facility agricultural products and the overall quality of farmers in the central and western regions of China, it is appropriate to transfer the whole technical system such as facility tomato, facility cucumber and so on. According to the varieties of characteristic agricultural products, cultivation models, quality control objectives to adapt to light, temperature, humidity and other parameters, as well as fertilizer, water, medicine and other input plans, a multi-factor coupling model suitable for a specific planting area is generated and long-term production verification and model correction are carried out. And popularize it in a wider area, making full use of the advantages of intelligent equipment and data elements will promote the realization of light simplification of production equipment, scene of intelligent technology, diversification of service models, on-line quality control, large-scale production of digital intelligence, and value of data elements, further cultivate facilities to produce new quality productivity. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Grain Production Big Data Platform: Progress and Prospects YANG Guijun, ZHAO Chunjiang, YANG Xiaodong, YANG Hao, HU Haitang, LONG Huiling, QIU Zhengjun, LI Xian, JIANG Chongya, SUN Liang, CHEN Lei, ZHOU Qingbo, HAO Xingyao, GUO Wei, WANG Pei, GAO Meiling Smart Agriculture    2025, 7 (2): 1-12.   DOI: 10.12133/j.smartag.SA202409014 Abstract （534）   HTML （81）    PDF（pc） （1811KB）（2105）       Knowledge map    Save [Significance] The explosive development of agricultural big data has accelerated agricultural production into a new era of digitalization and intelligentialize. Agricultural big data is the core element to promote agricultural modernization and the foundation of intelligent agriculture. As a new productive forces, big data enhances the comprehensive intelligent management decision-making during the whole process of grain production. But it faces the problems such as the indistinct management mechanism of grain production big data resources, the lack of the full-chain decision-making algorithm system and big data platform for the whole process and full elements of grain production. [Progress] Grain production big data platform is a comprehensive service platform that uses modern information technologies such as big data, Internet of Things (IoT), remote sensing and cloud computing to provide intelligent decision-making support for the whole process of grain production based on intelligent algorithms for data collection, processing, analysis and monitoring related to grain production. In this paper, the progress and challenges in grain production big data, monitoring and decision-making algorithms are reviewed, as well as big data platforms in China and worldwide. With the development of the IoT and high-resolution multi-modal remote sensing technology, the massive agricultural big data generated by the "Space-Air-Ground" Integrated Agricultural Monitoring System, has laid an important foundation for smart agriculture and promoted the shift of smart agriculture from model-driven to data-driven. However, there are still some issues in field management decision-making, such as the requirements for high spatio-temporal resolution and timeliness of the information are difficult to meet, and the algorithm migration and localization methods based on big data need to be studied. In addition, the agricultural machinery operation and spatio-temporal scheduling algorithm based on remote sensing and IoT monitoring information to determine the appropriate operation time window and operation prescription, needs to be further developed, especially the cross-regional scheduling algorithm of agricultural machinery for summer harvest in China. Aiming to address the issues of non-bi-connected monitoring and decision-making algorithms in grain production, as well as the insufficient integration of agricultural machinery and information perception, a framework for the grain production big data intelligent platform based on digital twins is proposed. The platform leverages multi-source heterogeneous grain production big data and integrates a full-chain suit of standardized algorithms, including data acquisition, information extraction, knowledge map construction, intelligent decision-making, full-chain collaboration of agricultural machinery operations. It covers the typical application scenarios such as irrigation, fertilization, pests and disease management, emergency response to drought and flood disaster, all enabled by digital twins technology. [Conclusions and Prospects] The suggestions and trends for development of grain production big data platform are summarized in three aspects: (1) Creating an open, symbiotic grain production big data platform, with core characteristics such as open interface for crop and environmental sensors, maturity grading and a cloud-native packaging mechanism for core algorithms, highly efficient response to data and decision services; (2) Focusing on the typical application scenarios of grain production, take the exploration of technology integration and bi-directional connectivity as the base, and the intelligent service as the soul of the development path for the big data platform research; (3) The data-algorithm-service self-organizing regulation mechanism, the integration of decision-making information with the intelligent equipment operation, and the standardized, compatible and open service capabilities, can form the new quality productivity to ensure food safety, and green efficiency grain production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Fast Extracting Method for Strawberry Leaf Age and Canopy Width Based on Instance Segmentation Technology FAN Jiangchuan, WANG Yuanqiao, GOU Wenbo, CAI Shuangze, GUO Xinyu, ZHAO Chunjiang Smart Agriculture    2024, 6 (2): 95-106.   DOI: 10.12133/j.smartag.SA202310014 Abstract （877）   HTML （48）    PDF（pc） （1903KB）（2098）       Knowledge map    Save [Objective] There's a growing demand among plant cultivators and breeders for efficient methods to acquire plant phenotypic traits at high throughput, facilitating the establishment of mappings from phenotypes to genotypes. By integrating mobile phenotyping platforms with improved instance segmentation techniques, researchers have achieved a significant advancement in the automation and accuracy of phenotypic data extraction. Addressing the need for rapid extraction of leaf age and canopy width phenotypes in strawberry plants cultivated in controlled environments, this study introduces a novel high-throughput phenotyping extraction approach leveraging a mobile phenotyping platform and instance segmentation technology. [Methods] Data acquisition was conducted using a compact mobile phenotyping platform equipped with an array of sensors, including an RGB sensor, and edge control computers, capable of capturing overhead images of potted strawberry plants in greenhouses. Targeted adjustments to the network structure were made to develop an enhanced convolutional neural network (Mask R-CNN) model for processing strawberry plant image data and rapidly extracting plant phenotypic information. The model initially employed a split-attention networks (ResNeSt) backbone with a group attention module, replacing the original network to improve the precision and efficiency of image feature extraction. During training, the model adopted the Mosaic method, suitable for instance segmentation data augmentation, to expand the dataset of strawberry images. Additionally, it optimized the original cross-entropy classification loss function with a binary cross-entropy loss function to achieve better detection accuracy of plants and leaves. Based on this, the improved Mask R-CNN description involves post-processing of training results. It utilized the positional relationship between leaf and plant masks to statistically count the number of leaves. Additionally, it employed segmentation masks and image calibration against true values to calculate the canopy width of the plant. [Results and Discussions] This research conducted a thorough evaluation and comparison of the performance of an improved Mask R-CNN model, underpinned by the ResNeSt-101 backbone network. This model achieved a commendable mask accuracy of 80.1% and a detection box accuracy of 89.6%. It demonstrated the ability to efficiently estimate the age of strawberry leaves, demonstrating a high plant detection rate of 99.3% and a leaf count accuracy of 98.0%. This accuracy marked a significant improvement over the original Mask R-CNN model and meeting the precise needs for phenotypic data extraction. The method displayed notable accuracy in measuring the canopy widths of strawberry plants, with errors falling below 5% in about 98.1% of cases, highlighting its effectiveness in phenotypic dimension evaluation. Moreover, the model operated at a speed of 12.9 frames per second (FPS) on edge devices, effectively balancing accuracy and operational efficiency. This speed proved adequate for real-time applications, enabling rapid phenotypic data extraction even on devices with limited computational capabilitie. [Conclusions] This study successfully deployed a mobile phenotyping platform combined with instance segmentation techniques to analyze image data and extract various phenotypic indicators of strawberry plant. Notably, the method demonstrates remarkable robustness. The seamless fusion of mobile platforms and advanced image processing methods not only enhances efficiency but also ignifies a shift towards data-driven decision-making in agriculture. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Pig Back Transformer: Automatic 3D Pig Body Measurement Model WANG Yuxiao, SHI Yuanyuan, CHEN Zhaoda, WU Zhenfang, CAI Gengyuan, ZHANG Sumin, YIN Ling Smart Agriculture    2024, 6 (4): 76-90.   DOI: 10.12133/j.smartag.SA202401023 Abstract （1373）   HTML （46）    PDF（pc） （2776KB）（2020）       Knowledge map    Save [Objective] Nowadays most no contact body size measurement studies are based on point cloud segmentation method, they use a trained point cloud segmentation neural network to segment point cloud of pigs, then locate measurement points based on them. But point cloud segmentation neural network always need a larger graphics processing unit (GPU) memory, moreover, the result of the measurement key point still has room of improvement. This study aims to design a key point generating neural network to extract measurement key points from pig's point cloud. Reducing the GPU memory usage and improve the result of measurement points at the same time, improve both the efficiency and accuracy of the body size measurement. [Methods] A neural network model was proposed using improved Transformer attention mechanic called Pig Back Transformer for generating key points and back orientation points which were related to pig body dimensions. In the first part of the network, it was introduced an embedding structure for initial feature extraction and a Transformer encoder structure with edge attention which was a self-attention mechanic improved from Transformer's encoder. The embedding structure using two shared multilayer perceptron (MLP) and a distance embedding algorithm, it takes a set of points from the edge of pig back's point cloud as input and then extract information from the edge points set. In the encoder part, information about the offset distances between edge points and mass point which were their feature that extracted by the embedding structure mentioned before incorporated. Additionally, an extraction algorithm for back edge point was designed for extracting edge points to generate the input of the neural network model. In the second part of the network, it was proposed a Transformer encoder with improved self-attention called back attention. In the design of back attention, it also had an embedding structure before the encoder structure, this embedding structure extracted features from offset values, these offset values were calculated by the points which are none-edge and down sampled by farthest point sampling (FPS) to both the relative centroid point and model generated global key point from the first part that introduced before. Then these offset values were processed with max pooling with attention generated by the extracted features of the points' axis to extract more information that the original Transformer encoder couldn't extract with the same number of parameters. The output part of the model was designed to generate a set of offsets of the key points and points for back direction fitting, than add the set offset to the global key point to get points for pig body measurements. At last, it was introduced the methods for calculating body dimensions which were length, height, shoulder width, abdomen width, hip width, chest circumference and abdomen circumference using key points and back direction fitting points. [Results and Discussions] In the task of generating key points and points for back direction fitting, the improved Pig Back Transformer performed the best in the accuracy wise in the models tested with the same size of parameters, and the back orientation points generated by the model were evenly distributed which was a good preparation for a better body length calculation. A melting test for edge detection part with two attention mechanic and edge trim method both introduced above had being done, when the edge detection and the attention mechanic got cut off, the result had been highly impact, it made the model couldn't perform as well as before, when the edge trim method of preprocessing part had been cut off, there's a moderate impact on the trained model, but it made the loss of the model more inconsistence while training than before. When comparing the body measurement algorithm with human handy results, the relative error in length was 0.63%, which was an improvement compared to other models. On the other hand, the relative error of shoulder width, abdomen width and hip width had edged other models a little but there was no significant improvement so the performance of these measurement accuracy could be considered negligible, the relative error of chest circumference and abdomen circumference were a little bit behind by the other methods existed, it's because the calculate method of circumferences were not complicated enough to cover the edge case in the dataset which were those point cloud that have big holes in the bottom of abdomen and chest, it impacted the result a lot. [Conclusions] The improved Pig Back Transformer demonstrates higher accuracy in generating key points and is more resource-efficient, enabling the calculation of more accurate pig body measurements. And provides a new perspective for non-contact livestock body size measurements. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Detection Method for Dragon Fruit in Natural Environment Based on Improved YOLOX SHANG Fengnan, ZHOU Xuecheng, LIANG Yingkai, XIAO Mingwei, CHEN Qiao, LUO Chendi Smart Agriculture    2022, 4 (3): 120-131.   DOI: 10.12133/j.smartag.SA202207001 Abstract （802）   HTML （68）    PDF（pc） （2267KB）（2005）       Knowledge map    Save Dragon fruit detection in natural environment is the prerequisite for fruit harvesting robots to perform harvesting. In order to improve the harvesting efficiency, by improving YOLOX (You Only Look Once X) network, a target detection network with an attention module was proposed in this research. As the benchmark, YOLOX-Nano network was chose to facilitate deployment on embedded devices, and the convolutional block attention module (CBAM) was added to the backbone feature extraction network of YOLOX-Nano, which improved the robustness of the model to dragon fruit target detection to a certain extent. The correlation of features between different channels was learned by weight allocation coefficients of features of different scales, which were extracted for the backbone network. Moreover, the transmission of deep information of network structure was strengthened, which aimed at reducing the interference of dragon fruit recognition in the natural environment as well as improving the accuracy and speed of detection significantly. The performance evaluation and comparison test of the method were carried out. The results showed that, after training, the dragon fruit target detection network got an AP0.5 value of 98.9% in the test set, an AP0.5:0.95 value of 72.4% and F1 score was 0.99. Compared with other YOLO network models under the same experimental conditions, on the one hand, the improved YOLOX-Nano network model proposed in this research was more lightweight, on the other hand, the detection accuracy of this method surpassed that of YOLOv3, YOLOv4 and YOLOv5 respectively. The average detection accuracy of the improved YOLOX-Nano target detection network was the highest, reaching 98.9%, 26.2% higher than YOLOv3, 9.8% points higher than YOLOv4-Tiny, and 7.9% points higher than YOLOv5-S. Finally, real-time tests were performed on videos with different input resolutions. The improved YOLOX-Nano target detection network proposed in this research had an average detection time of 21.72 ms for a single image. In terms of the size of the network model was only 3.76 MB, which was convenient for deployment on embedded devices. In conclusion, not only did the improved YOLOX-Nano target detection network model accurately detect dragon fruit under different lighting and occlusion conditions, but the detection speed and detection accuracy showed in this research could able to meet the requirements of dragon fruit harvesting in natural environment requirements at the same time, which could provide some guidance for the design of the dragon fruit harvesting robot. Reference | Related Articles | Metrics | Comments（0）

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A Hyperspectral Image-Based Method for Estimating Water and Chlorophyll Contents in Maize Leaves under Drought Stress WANG Jingyong, ZHANG Mingzhen, LING Huarong, WANG Ziting, GAI Jingyao Smart Agriculture    2023, 5 (3): 142-153.   DOI: 10.12133/j.smartag.SA202308018 Abstract （533）   HTML （50）    PDF（pc） （2191KB）（1958）       Knowledge map    Save [Objectives] Chlorophyll content and water content are key physiological indicators of crop growth, and their non-destructive detection is a key technology to realize the monitoring of crop growth status such as drought stress. This study took maize as an object to develop a hyperspectral-based approach for the rapid and non-destructive acquisition of the leaf chlorophyll content and water content for drought stress assessment. [Methods] Drought treatment experiments were carried out in a greenhouse of the College of Agriculture, Guangxi University. Maize plants were subjected to drought stress treatment at the seedling stage (four leaves). Four drought treatments were set up for normal water treatment [CK], mild drought [W1], moderate drought [W2], and severe drought [W3], respectively. Leaf samples were collected at the 3rd, 6th, and 9th days after drought treatments, and 288 leaf samples were collected in total, with the corresponding chlorophyll content and water content measured in a standard laboratory protocol. A pair of push-broom hyperspectral cameras were used to collect images of the 288 seedling maize leaf samples, and image processing techniques were used to extract the mean spectra of the leaf lamina part. The algorithm flow framework of "pre-processing - feature extraction - machine learning inversion" was adopted for processing the extracted spectral data. The effects of different pre-processing methods, feature wavelength extraction methods and machine learning regression models were analyzed systematically on the prediction performance of chlorophyll content and water content, respectively. Accordingly, the optimal chlorophyll content and water content inversion models were constructed. Firstly, 70% of the spectral data was randomly sampled and used as the training dataset for training the inversion model, whereas the remaining 30% was used as the testing dataset to evaluate the performance of the inversion model. Subsequently, the effects of different spectral pre-processing methods on the prediction performance of chlorophyll content and water content were compared. Different feature wavelengths were extracted from the optimal pre-processed spectra using different algorithms, then their capabilities in preserve the information useful for the inversion of leaf chlorophyll content and water content were compared. Finally, the performances of different machine learning regression model were compared, and the optimal inversion model was constructed and used to visualize the chlorophyll content and water content. Additionally, the construction of vegetation coefficients were explored for the inversion of chlorophyll content and water content and evaluated their inversion ability. The performance evaluation indexes used include determination coefficient and root mean squared error (RMSE). [Results and Discussions] With the aggravation of stress, the reflectivity of leaves in the wavelength range of 400~1700 nm gradually increased with the degree of drought stress. For the inversion of leaf chlorophyll content and water content, combining stepwise regression (SR) feature extraction with Stacking regression could obtain an optimal performance for chlorophyll content prediction, with an R2 of 0.878 and an RMSE of 0.317 mg/g. Compared with the full-band stacking model, SR-Stacking not only improved R2 by 2.9%, reduced RMSE by 0.0356mg/g, but also reduced the number of model input variables from 1301 to 9. Combining the successive projection algorithm (SPA) feature extraction with Stacking regression could obtain the optimal performance for water content prediction, with an R2 of 0.859 and RMSE of 3.75%. Compared with the full-band stacking model, SPA-Stacking not only increased R2 by 0.2%, reduced RMSE by 0.03%, but also reduced the number of model input variables from 1301 to 16. As the newly constructed vegetation coefficients, normalized difference vegetation index(NDVI) [(R410-R559)/(R410+R559)] and ratio index (RI) (R400/R1171) had the highest accuracy and were significantly higher than the traditional vegetation coefficients for chlorophyll content and water content inversion, respectively. Their R2 were 0.803 and 0.827, and their RMSE were 0.403 mg/g and 3.28%, respectively. The chlorophyll content and water content of leaves were visualized. The results showed that the physiological parameters of leaves could be visualized and the differences of physiological parameters in different regions of the same leaves can be found more intuitively and in detail. [Conclusions] The inversion models and vegetation indices constructed based on hyperspectral information can achieve accurate and non-destructive measurement of chlorophyll content and water content in maize leaves. This study can provide a theoretical basis and technical support for real-time monitoring of corn growth status. Through the leaf spectral information, according to the optimal model, the water content and chlorophyll content of each pixel of the hyperspectral image can be predicted, and the distribution of water content and chlorophyll content can be intuitively displayed by color. Because the field environment is more complex, transfer learning will be carried out in future work to improve its generalization ability in different environments subsequently and strive to develop an online monitoring system for field drought and nutrient stress. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Root Image Segmentation Method Based on Improved UNet and Transfer Learning TANG Hui, WANG Ming, YU Qiushi, ZHANG Jiaxi, LIU Liantao, WANG Nan Smart Agriculture    2023, 5 (3): 96-109.   DOI: 10.12133/j.smartag.SA202308003 Abstract （577）   HTML （65）    PDF（pc） （2442KB）（1918）       Knowledge map    Save [Objective] The root system is an important component of plant composition, and its growth and development are crucial for plants. Root image segmentation is an important method for obtaining root phenotype information and analyzing root growth patterns. Research on root image segmentation still faces difficulties, because of the noise and image quality limitations, the intricate and diverse soil environment, and the ineffectiveness of conventional techniques. This paper proposed a multi-scale feature extraction root segmentation algorithm that combined data augmentation and transfer learning to enhance the generalization and universality of the root image segmentation models in order to increase the speed, accuracy, and resilience of root image segmentation. [Methods] Firstly, the experimental datasets were divided into a single dataset and a mixed dataset. The single dataset acquisition was obtained from the experimental station of Hebei Agricultural University in Baoding city. Additionally, a self-made RhizoPot device was used to collect images with a resolution pixels of 10,200×14,039, resulting in a total of 600 images. In this experiment, 100 sheets were randomly selected to be manually labeled using Adobe Photoshop CC2020 and segmented into resolution pixels of 768×768, and divided into training, validation, and test sets according to 7:2:1. To increase the number of experimental samples, an open source multi-crop mixed dataset was obtained in the network as a supplement, and it was reclassified into training, validation, and testing sets. The model was trained using the data augmentation strategy, which involved performing data augmentation operations at a set probability of 0.3 during the image reading phase, and each method did not affect the other. When the probability was less than 0.3, changes would be made to the image. Specific data augmentation methods included changing image attributes, randomly cropping, rotating, and flipping those images. The UNet structure was improved by designing eight different multi-scale image feature extraction modules. The module structure mainly included two aspects: Image convolution and feature fusion. The convolution improvement included convolutional block attention module (CBAM), depthwise separable convolution (DP Conv), and convolution (Conv). In terms of feature fusion methods, improvements could be divided into concatenation and addition. Subsequently, ablation tests were conducted based on a single dataset, data augmentation, and random loading of model weights, and the optimal multi-scale feature extraction module was selected and compared with the original UNet. Similarly, a single dataset, data augmentation, and random loading of model weights were used to compare and validate the advantages of the improved model with the PSPNet, SegNet, and DeeplabV3Plus algorithms. The improved model used pre-trained weights from a single dataset to load and train the model based on mixed datasets and data augmentation, further improving the model's generalization ability and root segmentation ability. [Results and Discussions] The results of the ablation tests indicated that Conv_ 2+Add was the best improved algorithm. Compared to the original UNet, the mIoU, mRecall, and root F1 values of the model increased by 0.37%, 0.99%, and 0.56%, respectively. And, comparative experiments indicate Unet+Conv_2+Add model was superior to the PSPNet, SegNet, and DeeplabV3Plus models, with the best evaluation results. And the values of mIoU, mRecall, and the harmonic average of root F1 were 81.62%, 86.90%, and 77.97%, respectively. The actual segmented images obtained by the improved model were more finely processed at the root boundary compared to other models. However, for roots with deep color and low contrast with soil particles, the improved model could only achieve root recognition and the recognition was sparse, sacrificing a certain amount of information extraction ability. This study used the root phenotype evaluation software Rhizovision to analyze the root images of the Unet+Conv_2+Add improved model, PSPNet, SegNet, and DeeplabV3Plu, respectively, to obtain the values of the four root phenotypes (total root length, average diameter, surface area, and capacity), and the results showed that the average diameter and surface area indicator values of the improved model, Unet+Conv_2+Add had the smallest differences from the manually labeled indicator values and the SegNet indicator values for the two indicators. Total root length and volume were the closest to those of the manual labeling. The results of transfer learning experiments proved that compared with ordinary training, the transfer training of the improved model UNet+Conv_2+Add increased the IoU value of the root system by 1.25%. The Recall value of the root system was increased by 1.79%, and the harmonic average value of F1 was increased by 0.92%. Moreover, the overall convergence speed of the model was fast. Compared with regular training, the transfer training of the original UNet improved the root IoU by 0.29%, the root Recall by 0.83%, and the root F1 value by 0.21%, which indirectly confirmed the effectiveness of transfer learning. [Conclusions] The multi-scale feature extraction strategy proposed in this study can accurately and efficiently segment roots, and further improve the model's generalization ability using transfer learning methods, providing an important research foundation for crop root phenotype research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Corn and Soybean Futures Price Intelligent Forecasting Based on Deep Learning XU Yulin, KANG Mengzhen, WANG Xiujuan, HUA Jing, WANG Haoyu, SHEN Zhen Smart Agriculture    2022, 4 (4): 156-163.   DOI: 10.12133/j.smartag.SA20220712 Abstract （1286）   HTML （146）    PDF（pc） （872KB）（1918）       Knowledge map    Save Corn and soybean are upland grain in the same season, and the contradiction of scrambling for land between corn and soybean is prominent in China, so it is necessary to explore the price relations between corn and soybean. In addition, agricultural futures have the function of price discovery compared with the spot. Therefore, the analysis and prediction of corn and soybean futures prices are of great significance for the management department to adjust the planting structure and for farmers to select the crop varieties. In this study, the correlation between corn and soybean futures prices was analyzed, and it was found that the corn and soybean futures prices have a strong correlation by correlation test, and soybean futures price is the Granger reason of corn futures price by Granger causality test. Then, the corn and soybean futures prices were predicted using a long short-term memory (LSTM) model. To optimize the futures price prediction model performance, Attention mechanism was introduced as Attention-LSTM to assign weights to the outputs of the LSTM model at different times. Specifically, LSTM model was used to process the input sequence of futures prices, the Attention layer assign different weights to the outputs, and then the model output the prediction results after a layer of linearity. The experimental results showed that Attention-LSTM model could significantly improve the prediction performance of both corn and soybean futures prices compared to autoregressive integrated moving average model (ARIMA), support vector regression model (SVR), and LSTM. For example, mean absolute error (MAE) was improved by 3.8% and 3.3%, root mean square error (RMSE) was improved by 0.6% and 1.8% and mean absolute error percentage (MAPE) was improved by 4.8% and 2.9% compared with a single LSTM, respectively. Finally, the corn futures prices were forecasted using historical corn and soybean futures prices together. Specifically, two LSTM models were used to process the input sequences of corn futures prices and soybean futures prices respectively, two parameters were trained to perform a weighted summation of the output of two LSTM models, and the prediction results were output by the model after a layer of linearity. The experimental results showed that MAE was improved by 6.9%, RMSE was improved by 1.1% and MAPE was improved by 5.3% compared with the LSTM model using only corn futures prices. The results verify the strong correlation between corn and soybean futures prices at the same time. In conclusion, the results verify the Attention-LSTM model can improve the performances of soybean and corn futures price forecasting compared with the general prediction model, and the combination of related agricultural futures price data can improve the prediction performances of agricultural product futures forecasting model. Reference | Related Articles | Metrics | Comments（0）

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Three-Dimensional Virtual Orchard Construction Method Based on Laser Point Cloud FENG Han, ZHANG Hao, WANG Zi, JIANG Shijie, LIU Weihong, ZHOU Linghui, WANG Yaxiong, KANG Feng, LIU Xingxing, ZHENG Yongjun Smart Agriculture    2022, 4 (3): 12-23.   DOI: 10.12133/j.smartag.SA202207002 Abstract （1044）   HTML （114）    PDF（pc） （2426KB）（1908）       Knowledge map    Save To solve the problems of low level of digitalization of orchard management and relatively single construction method, a three-dimensional virtual orchard construction method based on laser point cloud was proposed in this research. First, the hand-held 3D point cloud acquistion equipment (3D-BOX) combined with the lidar odometry and mapping (SLAM-LOAM) algorithm was used to complete the acquisition of the point cloud data set of orchard; then the outliers and noise points of the point cloud data were removed by using the statistical filtering algorithm, which was based on the K-neighbor distance statistical method. To achieve this, a distance threshold model for removing noise points was established. When a discrete point exceeded, it would be marked as an outlier, and the point was separated from the point cloud dataset to achieve the effect of discrete point filtering. The VoxelGrid filter was used for down sampling, the cloth simulation filtering (CSF) cloth simulation algorithm was used to calculate the distance between the cloth grid points and the corresponding laser point cloud, and the distinction between ground points and non-ground points was achieved by dividing the distance threshold, and when combined with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, ground removal and cluster segmentation of orchard were realized; finally, the Unity3D engine was used to build a virtual orchard roaming scene, and convert the real-time GPS data of the operating equipment from the WGS-84 coordinate system to the Gauss projection plane coordinate system through Gaussian projection forward calculation. The real-time trajectory of the equipment was displayed through the LineRenderer, which realized the visual display of the motion trajectory control and operation trajectory of the working machine. In order to verify the effectiveness of the virtual orchard construction method, the test of orchard construction method was carried out in the Begonia fruit and the mango orchard. The results showed that the proposed point cloud data processing method could achieve the accuracy of cluster segmentation of Begonia fruit trees and mango trees 95.3% and 98.2%, respectively. Compared with the row spacing and plant spacing of fruit trees in the actual mango orchard, the average inter-row error of the virtual mango orchard was about 3.5%, and the average inter-plant error was about 6.6%. And compared the virtual orchard constructed by Unity3D with the actual orchard, the proposed method can effectively reproduce the actual three-dimensional situation of the orchard, and obtain a better visualization effect, which provides a technical solution for the digital modeling and management of the orchard. Reference | Related Articles | Metrics | Comments（0）

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Automatic Spraying Technology and Facilities for Pipeline Spraying in Mountainous Orchards SONG Shuran, HU Shengyang, SUN Daozong, DAI Qiufang, XUE Xiuyun, XIE Jiaxing, LI Zhen Smart Agriculture    2022, 4 (3): 86-94.   DOI: 10.12133/j.smartag.SA202205005 Abstract （568）   HTML （42）    PDF（pc） （1218KB）（1866）       Knowledge map    Save The orchard in the mountainous area is rugged and steep, and there is no road for large-scale plant protection machinery traveling in the orchard, so it is difficult for mobile spraying machinery to enter. In order to solve the above problems, the automatic pipeline spraying technology and facilities were studied. A pipeline automatic spraying facility suitable for mountainous orchards was designed, which included spraying head, field spraying pipeline, automatic spraying controller and spraying groups. The spraying head was composed of a spraying unit and a constant pressure control system, which pressurized the pesticide liquid and stabilized the liquid pressure according to the preset pressure value to ensure a better atomization effect. Field spraying pipeline consisted of main pipeline, valves and spraying groups. In order to perform automatic spraying, a solenoid valve was installed between the main pipeline and each spraying group, and the automatic spraying operation of each spraying group was controlled automatically by the opening or closing of the solenoid valve. An automatic spraying controller composed of main controller, solenoid valve driving circuit, solenoid valve controlling node and power supplying unit was developed, and the controlling software was also programmed in this research. The main controller had manual and automatic two working modes. The solenoid valve controlling node was used to send wireless signals to the main controller and receive wireless signals from the main controller, and open or close the corresponding solenoid valve according to the received control signal. During the spraying operation, the pesticide liquid flowed into the orchard from the spray head through the pipeline. The automatic spray controller was used to control the solenoid valve to open or close the spray group one by one, and implement manual control or automatic control of spraying. In order to determine the continuous opening time of the solenoid valve, an effectiveness of the spray test was carried out. The spraying test results showed that spraying effectiveness could be guaranteed by opening solenoid valve for 8 s continuously. The efficiency of this pipeline automatic spraying facility was 2.61 hm2/h, which was 45－150 times that of manual spraying, and 2.1 times that of unmanned aerial vehicle spraying. The automatic pipeline spraying technology in mountainous orchards had obvious advantages in the timeliness of pest controlling. This research can provide references and ideas for the development of spray technology and intelligent spraying facilities in mountainous orchards. Reference | Related Articles | Metrics | Comments（0）

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Automatic Measurement Method of Beef Cattle Body Size Based on Multimodal Image Information and Improved Instance Segmentation Network WENG Zhi, FAN Qi, ZHENG Zhiqiang Smart Agriculture    2024, 6 (4): 64-75.   DOI: 10.12133/j.smartag.SA202310007 Abstract （1292）   HTML （55）    PDF（pc） （3345KB）（1836）       Knowledge map    Save [Objective] The body size parameter of cattle is a key indicator reflecting the physical development of cattle, and is also a key factor in the cattle selection and breeding process. In order to solve the demand of measuring body size of beef cattle in the complex environment of large-scale beef cattle ranch, an image acquisition device and an automatic measurement algorithm of body size were designed. [Methods] Firstly, the walking channel of the beef cattle was established, and when the beef cattle entered the restraining device through the channel, the RGB and depth maps of the image on the right side of the beef cattle were acquired using the Inter RealSense D455 camera. Secondly, in order to avoid the influence of the complex environmental background, an improved instance segmentation network based on Mask2former was proposed, adding CBAM module and CA module, respectively, to improve the model's ability to extract key features from different perspectives, extracting the foreground contour from the 2D image of the cattle, partitioning the contour, and comparing it with other segmentation algorithms, and using curvature calculation and other mathematical methods to find the required body size measurement points. Thirdly, in the processing of 3D data, in order to solve the problem that the pixel point to be measured in the 2D RGB image was null when it was projected to the corresponding pixel coordinates in the depth-valued image, resulting in the inability to calculate the 3D coordinates of the point, a series of processing was performed on the point cloud data, and a suitable point cloud filtering and point cloud segmentation algorithm was selected to effectively retain the point cloud data of the region of the cattle's body to be measured, and then the depth map was 16. Then the depth map was filled with nulls in the field to retain the integrity of the point cloud in the cattle body region, so that the required measurement points could be found and the 2D data could be returned. Finally, an extraction algorithm was designed to combine 2D and 3D data to project the extracted 2D pixel points into a 3D point cloud, and the camera parameters were used to calculate the world coordinates of the projected points, thus automatically calculating the body measurements of the beef cattle. [Results and Discussions] Firstly, in the part of instance segmentation, compared with the classical Mask R-CNN and the recent instance segmentation networks PointRend and Queryinst, the improved network could extract higher precision and smoother foreground images of cattles in terms of segmentation accuracy and segmentation effect, no matter it was for the case of occlusion or for the case of multiple cattles. Secondly, in three-dimensional data processing, the method proposed in the study could effectively extract the three-dimensional data of the target area. Thirdly, the measurement error of body size was analysed, among the four body size measurement parameters, the smallest average relative error was the height of the cross section, which was due to the more prominent position of the cross section, and the different standing positions of the cattle have less influence on the position of the cross section, and the largest average relative error was the pipe circumference, which was due to the influence of the greater overlap of the two front legs, and the higher requirements for the standing position. Finally, automatic body measurements were carried out on 137 beef cattle in the ranch, and the automatic measurements of the four body measurements parameters were compared with the manual measurements, and the results showed that the average relative errors of body height, cross section height, body slant length, and tube girth were 4.32%, 3.71%, 5.58% and 6.25%, respectively, which met the needs of the ranch. The shortcomings were that fewer body-size parameters were measured, and the error of measuring circumference-type body-size parameters was relatively large. Later studies could use a multi-view approach to increase the number of body rule parameters to be measured and improve the accuracy of the parameters in the circumference category. [Conclusions] The article designed an automatic measurement method based on two-dimensional and three-dimensional contactless body measurements of beef cattle. Moreover, the innovatively proposed method of measuring tube girth has higher accuracy and better implementation compared with the current research on body measurements in beef cattle. The relative average errors of the four body tape parameters meet the needs of pasture measurements and provide theoretical and practical guidance for the automatic measurement of body tape in beef cattle. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Evaluation and Countermeasures on the Development Level of Intelligent Cold Chain in China YANG Lin, YANG Bin, REN Qingshan, YANG Xinting, HAN Jiawei Smart Agriculture    2023, 5 (1): 22-33.   DOI: 10.12133/j.smartag.SA202302003 Abstract （781）   HTML （145）    PDF（pc） （1380KB）（1766）       Knowledge map    Save The new generation of information technology has led to the rapid development of the intelligent level of the cold chain, and the precise control of the development level of the smart cold chain is the prerequisite foundation and guarantee to achieve the key breakthrough of the technical bottleneck and the strategic layout of the development direction. Based on this, an evaluation index system for China's intelligent cold chain development from the dimensions of supply capacity, storage capacity, transportation capacity, economic efficiency and informationization level was conducted. The entropy weight method combined with the technique for order preference by similarity to ideal solution (TOPSIS) was used to quantitatively evaluate the development of intelligent cold chain in 30 Chinese provinces and cities (excluding Tibet, Hong Kong, Macao and Taiwan) from 2017 to 2021. The quantitative evaluation of the level of intelligent cold chain development was conducted. The impact of the evaluation indicators on different provinces and cities was analysed by exploratory spatial data analyses (ESDA) and geographically weighted regression (GWR). The results showed that indicators such as economic development status, construction of supporting facilities and informationization level had greater weight and played a more important role in influencing the construction of intelligent cold chain. The overall level of intelligent cold chain development in China is divided into four levels, with most cities at the third and fourth levels. Beijing and the eastern coastal provinces and cities generally have a better level of intelligent cold chain development, while the southwest and northwest regions are developing slowly. In terms of overall development, the overall development of China's intelligent cold chain is relatively backward, with insufficient inter-regional synergy. The global spatial autocorrelation analysis shows that the variability in the development of China's intelligent cold chain logistics is gradually becoming greater. Through the local spatial autocorrelation analysis, it can be seen that there is a positive spatial correlation between the provinces and cities in East China, and negative spatiality in North China and South China. After geographically weighted regression analysis, it can be seen that the evaluation indicators have significant spatial and temporal heterogeneity in 2017, with the degree of influence changing with spatial location and time, and the spatial and temporal heterogeneity of the evaluation indicators is not significant in 2021. In order to improve the overall development level of China's intelligent cold chain, corresponding development countermeasures are proposed to strengthen the construction of supporting facilities and promote the transformation and upgrading of information technology. This study can provide a scientific basis for the global planning, strategic layout and overall promotion of China's intelligent cold chain. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Path Planning and Motion Control Method for Sick and Dead Animal Transport Robots Integrating Improved A * Algorithm and Fuzzy PID XU Jishuang, JIAO Jun, LI Miao, LI Hualong, YANG Xuanjiang, LIU Xianwang, GUO Panpan, MA Zhirun Smart Agriculture    2023, 5 (4): 127-136.   DOI: 10.12133/j.smartag.SA202308001 Abstract （495）   HTML （18）    PDF（pc） （1068KB）（1748）       Knowledge map    Save [Objective] A key challenge for the harmless treatment center of sick and dead animal is to prevent secondary environmental pollution, especially during the process of transporting the animals from cold storage to intelligent treatment facilities. In order to solve this problem and achieve the intelligent equipment process of transporting sick and dead animal from storage cold storage to harmless treatment equipment in the harmless treatment center, it is necessary to conduct in-depth research on the key technical problems of path planning and autonomous walking of transport robots. [Methods] A * algorithm is mainly adopted for the robot path planning algorithm for indoor environments, but traditional A * algorithms have some problems, such as having many inflection points, poor smoothness, long calculation time, and many traversal nodes. In order to solve these problems, a path planning method for the harmless treatment of diseased and dead animal using transport robots based on the improved A algorithm was constructed, as well as a motion control method based on fuzzy proportional integral differential (PID). The Manhattan distance method was used to replace the heuristic function of the traditional A * algorithm, improving the efficiency of calculating the distance between the starting and ending points in the path planning process. Referring to the actual location of the harmless treatment site for sick and dead animal, vector cross product calculation was performed based on the vector from the starting point to the target point and the vector from the current position to the endpoint target. Additional values were added and dynamic adjustments were implemented, thereby changing the value of the heuristic function. In order to further improve the efficiency of path planning and reduce the search for nodes in the planning process, a method of adding function weights to the heuristic function was studied based on the actual situation on site, to change the weights according to different paths. When the current location node was relatively open, the search efficiency was improved by increasing the weight. When encountering situations such as corners, the weight was reduced to improve the credibility of the path. By improving the heuristic function, a driving path from the starting point to the endpoint was quickly obtained, but the resulting path was not smooth enough. Meanwhile, during the tracking process, the robot needs to accelerate and decelerate frequently to adapt to the path, resulting in energy loss. Therefore, according to the different inflection points and control points of the path, different orders of Bessel functions were introduced to smooth the planning process for the path, in order to achieve practical application results. By analyzing the kinematics of robot, the differential motion method of the track type was clarified. On this basis, a walking control algorithm for the robot based on fuzzy PID control was studied and proposed. Based on the actual operation status of the robot, the fuzzy rule conditions were recorded into a fuzzy control rule table, achieving online identification of the characteristic parameters of the robot and adjusting the angular velocity deviation of robot. When the robot controller received a fuzzy PID control signal, the angular velocity output from the control signal was converted into a motor rotation signal, which changed the motor speed on both sides of the robot to achieve differential control and adjust the steering of the robot. [Results and Discussions] Simulation experiments were conducted using the constructed environmental map obtained, verifying the effectiveness of the path planning method for the harmless treatment of sick and dead animal using the improved A algorithm. The comparative experiments between traditional A * algorithm and improved algorithm were conducted. The experimental results showed that the average traversal nodes of the improved A * algorithm decreased from 3 067 to 1 968, and the average time of the algorithm decreased from 20.34 s to 7.26 s. Through on-site experiments, the effectiveness and reliability of the algorithm were further verified. Different colors were used to identify the planned paths, and optimization comparison experiments were conducted on large angle inflection points, U-shaped inflection points, and continuous inflection points in the paths, verifying the optimization effect of the Bessel function on path smoothness. The experimental results showed that the path optimized by the Bessel function was smoother and more suitable for the walking of robot in practical scenarios. Fuzzy PID path tracking experiment results showed that the loading truck can stay close to the original route during both straight and turning driving, demonstrating the good effect of fuzzy PID on path tracking. Further experiments were conducted on the harmless treatment center to verify the effectiveness and practical application of the improved algorithm. Based on the path planning algorithm, the driving path of robot was quickly planned, and the fuzzy PID control algorithm was combined to accurately output the angular velocity, driving the robot to move. The transport robots quickly realized the planning of the transportation path, and during the driving process, could always be close to the established path, and the deviation error was maintained within a controllable range. [Conclusions] A path planning method for the harmless treatment of sick and dead animal using an transport robots based on an improved A * algorithm combined with a fuzzy PID motion control was proposed in this study. This method could effectively shorten the path planning time, reduce traversal nodes, and improve the efficiency and smoothness of path planning. 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Design and Test of Self-Propelled Orchard Multi-Station Harvesting Equipment MIAO Youyi, CHEN Hong, CHEN Xiaobing, TIAN Haoyu, YUAN Dong Smart Agriculture    2022, 4 (3): 42-52.   DOI: 10.12133/j.smartag.SA202206007 Abstract （626）   HTML （69）    PDF（pc） （1391KB）（1719）       Knowledge map    Save In order to solve the problems of high labor intensity, low efficiency of manual operation and lack of supporting machinery in the fruit harvesting of modern orchards, a self-propelled orchard multi-station harvesting equipment was designed in combination with the fruit tree dwarf anvil wide-row dense planting mode and agronomic planting requirements. The whole machine structure and working principle of the self-propelled orchard multi-station harvesting equipment were expounded. According to the environmental conditions of mountainous orchards, the crawler chassis structure was designed, and the working speed was 0~2 km/h. The operating platform including left extension platform and right extension platform was designed according to the difference of fruit tree row spacing, and the working width of the operating platform was 1500~2700 mm. In order to improve the working efficiency and ensure the same picking speed of upper and lower operators, the picking operation mode of "two sides, two heights and six stations" was proposed by comparing the difference in the working flexibility between the operator on the platform and the operator on the ground during the operation of the machine, and the in-and-out channels of fruit boxes and the automatic collection and packing device were designed. The front and rear unobstructed fruit box access system was composed of the front loading and unloading mechanism, the rear loading and unloading mechanism and the fruit box slide rail, which was convenient for the empty fruit box to enter the fruit loading station of the working platform from the front and unloading from the rear after the fruit was filled. Six sub-conveyor belts were designed to handle apples harvested by six non interacting operators at the same time. The prototype was test in the field, and the packing uniform distribution coefficient calculation method was proposed to evaluate the uniformity of fruit packing, and the performance of the prototype was comprehensively evaluated in combination with the fruit damage rate and packing speed. The results showed that, the designed self-propelled orchard multi-station harvesting equipment could synchronize with the six stations manual harvesting speed. At the same time, with the help of the expansion platform, the apple picking range covered the entire canopy of the fruit tree. The prototype worked smoothly, and the speed of each conveyor belt was in good coordination with manual picking, and there was no apple congestion occurred. The apple harvest damage rate was 4.67%, the packing uniform distribution coefficient was 1.475, and the packing speed was 72.9 apples per minute, which could meet the requirements of orchard harvest operation. Reference | Related Articles | Metrics | Comments（0）

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Research Advances and Prospect of Intelligent Monitoring Systems for the Physiological Indicators of Beef Cattle ZHANG Fan, ZHOU Mengting, XIONG Benhai, YANG Zhengang, LIU Minze, FENG Wenxiao, TANG Xiangfang Smart Agriculture    2024, 6 (4): 1-17.   DOI: 10.12133/j.smartag.SA202312001 Abstract （1240）   HTML （81）    PDF（pc） （1471KB）（1718）       Knowledge map    Save [Significance] The beef cattle industry plays a pivotal role in the development of China's agricultural economy and the enhancement of people's dietary structure. However, there exists a substantial disparity in feeding management practices and economic efficiency of beef cattle industry compared to developed countries. While the beef cattle industry in China is progressing towards intensive, modern, and large-scale development, it encounters challenges such as labor shortage and rising labor costs that seriously affect its healthy development. The determination of animal physiological indicators plays an important role in monitoring animal welfare and health status. Therefore, leveraging data collected from various sensors as well as technologies like machine learning, data mining, and modeling analysis enables automatic acquisition of meaningful information on beef cattle physiological indicators for intelligent management of beef cattle. In this paper, the intelligent monitoring technology of physiological indicators in beef cattle breeding process and its application value are systematically summarized, and the existing challenges and future prospects of intelligent beef cattle breeding process in China are prospected. [Progress] The methods of obtaining information on beef cattle physiological indicators include contact sensors worn on the body and non-contact sensors based on various image acquisitions. Monitoring the exercise behavior of beef cattle plays a crucial role in disease prevention, reproduction monitoring, and status assessment. The three-axis accelerometer sensor, which tracks the amount of time that beef cattle spend on lying, walking, and standing, is a widely used technique for tracking the movement behavior of beef cattle. Through machine vision analysis, individual recognition of beef cattle and identification of standing, lying down, and straddling movements can also be achieved, with the characteristics of non-contact, stress-free, low cost, and generating high data volume. Body temperature in beef cattle is associated with estrus, calving, and overall health. Sensors for monitoring body temperature include rumen temperature sensors and rectal temperature sensors, but there are issues with their inconvenience. Infrared temperature measurement technology can be utilized to detect beef cattle with abnormal temperatures by monitoring eye and ear root temperatures, although the accuracy of the results may be influenced by environmental temperature and monitoring distance, necessitating calibration. Heart rate and respiratory rate in beef cattle are linked to animal diseases, stress, and pest attacks. Monitoring heart rate can be accomplished through photoelectric volume pulse wave measurement and monitoring changes in arterial blood flow using infrared emitters and receivers. Respiratory rate monitoring can be achieved by identifying different nostril temperatures during inhalation and exhalation using thermal infrared imaging technology. The ruminating behavior of beef cattle is associated with health and feed nutrition. Currently, the primary tools used to detect rumination behavior are pressure sensors and three-axis accelerometer sensors positioned at various head positions. Rumen acidosis is a major disease in the rapid fattening process of beef cattle, however, due to limitations in battery life and electrode usage, real-time pH monitoring sensors placed in the rumen are still not widely utilized. Changes in animal physiology, growth, and health can result in alterations in specific components within body fluids. Therefore, monitoring body fluids or surrounding gases through biosensors can be employed to monitor the physiological status of beef cattle. By processing and analyzing the physiological information of beef cattle, indicators such as estrus, calving, feeding, drinking, health conditions, and stress levels can be monitored. This will contribute to the intelligent development of the beef cattle industry and enhance management efficiency. While there has been some progress made in developing technology for monitoring physiological indicators of beef cattle, there are still some challenges that need to be addressed. Contact sensors consume more energy which affects their lifespan. Various sensors are susceptible to environmental interference which affects measurement accuracy. Additionally, due to a wide variety of beef cattle breeds, it is difficult to establish a model database for monitoring physiological indicators under different feeding conditions, breeding stages, and breeds. Furthermore, the installation cost of various intelligent monitoring devices is relatively high, which also limits its utilization coverage. [Conclusion and Prospects] The application of intelligent monitoring technology for beef cattle physiological indicators is highly significance in enhancing the management level of beef cattle feeding. Intelligent monitoring systems and devices are utilized to acquire physiological behavior data, which are then analyzed using corresponding data models or classified through deep learning techniques to promptly monitor subtle changes in physiological indicators. This enables timely detection of sick, estrus, and calving cattle, facilitating prompt measures by production managers, reducing personnel workload, and improving efficiency. The future development of physiological indicators monitoring technologies in beef cattle primarily focuses on the following three aspects: (1) Enhancing the lifespan of contact sensors by reducing energy consumption, decreasing data transmission frequency, and improving battery life. (2) Integrating and analyzing various monitoring data from multiple perspectives to enhance the accuracy and utility value. (3) Strengthening research on non-contact, high-precision and automated analysis technologies to promote the precise and intelligent development within the beef cattle industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Imaging System for Plant Photosynthetic Phenotypes Incorporating Three-dimensional Structured Light and Chlorophyll Fluorescence SHU Hongwei, WANG Yuwei, RAO Yuan, ZHU Haojie, HOU Wenhui, WANG Tan Smart Agriculture    2024, 6 (1): 63-75.   DOI: 10.12133/j.smartag.SA202311018 Abstract （468）   HTML （40）    PDF（pc） （1819KB）（1696）       Knowledge map    Save Objective The investigation of plant photosynthetic phenotypes is essential for unlocking insights into plant physiological characteristics and dissecting morphological traits. However, traditional two-dimensional chlorophyll fluorescence imaging methods struggle to capture the complex three-dimensional spatial variations inherent in plant photosynthetic processes. To boost the efficacy of plant phenotyping and meet the increasingly demand for high-throughput analysis of photosynthetic phenotypes, the development and validation of a novel plant photosynthetic phenotype imaging system was explored, which uniquely combines three-dimensional structured light techniques with chlorophyll fluorescence technology. Methods The plant photosynthetic phenotype imaging system was composed of three primary parts: A tailored light source and projector, a camera, and a motorized filter wheel fitted with filters of various bandwidths, in addition to a terminal unit equipped with a development board and a touchscreen interface. The system was based on the principles and unique characteristics of chlorophyll fluorescence and structured light phase-shifted streak 3D reconstruction techniques. It utilized the custom-designed light source and projector, together with the camera's capability to choose specific wavelength bands, to its full potential. The system employed low-intensity white light within the 400–700 nm spectrum to elicit stable fluorescence, with blue light in the 440–450 nm range optimally triggering the fluorescence response. A projector was used to project dual-frequency, twelve-step phase-shifted stripes onto the plant, enabling the capture of both planar and stripe images, which were essential for the reconstruction of the plant's three-dimensional structure. An motorized filter wheel containing filters for red, green, blue, and near-infrared light, augmented by a filter less wheel for camera collaboration, facilitated the collection of images of plants at different wavelengths under varying lighting conditions. When illuminated with white light, filters corresponding to the red, green, and blue bands were applied to capture multiband images, resulting in color photographs that provides a comprehensive documentation of the plant's visual features. Upon exposure to blue light, the near-infrared filter was employed to capture near-infrared images, yielding data on chlorophyll fluorescence intensity. During the structured light streak projection, no filter was applied to obtain both planar and streak images of the plant, which were then employed in the 3D morphological reconstruction of the plant. The terminal, incorporating a development board and a touch screen, served as the control hub for the data acquisition and subsequent image processing within the plant photosynthetic phenotypic imaging system. It enabled the switching of light sources and the selection of camera bands through a combination of command and serial port control circuits. Following image acquisition, the data were transmitted back to the development board for analysis, processing, storage, and presentation. To validate the accuracy of 3D reconstruction and the reliability of photosynthetic efficiency assessments by the system, a prototype of the plant photosynthetic phenotypic imaging system was developed using 3D structured light and chlorophyll fluorescence technology, in accordance with the aforementioned methods, serving as an experimental validation platform. The accuracy of 3D reconstruction and the effectiveness of photosynthetic analysis capabilities of this imaging system were further confirmed through the analysis and processing of the experimental results, with comparative evaluations conducted against conventional 3D reconstruction methods and traditional chlorophyll fluorescence-based photosynthetic efficiency analyses. Results and Discussions The imaging system utilized for plant photosynthetic phenotypes incorporates a dual-frequency phase-shift algorithm to facilitate the reconstruction of three-dimensional (3D) plant phenotypes. Simultaneously, plant chlorophyll fluorescence images were employed to evaluate the plant's photosynthetic efficiency. This method enabled the analysis of the distribution of photosynthetic efficiency within a 3D space, offering a significant advancement over traditional plant photosynthetic imaging techniques. The 3D phenotype reconstructed using this method exhibits high precision, with an overall reconstruction accuracy of 96.69%. The total error was merely 3.31%, and the time required for 3D reconstruction was only 1.11 s. A comprehensive comparison of the 3D reconstruction approach presented with conventional methods had validated the accuracy of this technique, laying a robust foundation for the precise estimation of a plant's 3D photosynthetic efficiency. In the realm of photosynthetic efficiency analysis, the correlation coefficient between the photosynthetic efficiency values inferred from the chlorophyll fluorescence image analysis and those determined by conventional analysis exceeded 0.9. The experimental findings suggest a significant correlation between the photosynthetic efficiency values obtained using the proposed method and those from traditional methods, which could be characterized by a linear relationship, thereby providing a basis for more precise predictions of plant photosynthetic efficiency. Conclusions The method melds the 3D phenotype of plants with an analysis of photosynthetic efficiency, allowing for a more holistic assessment of the spatial heterogeneity in photosynthetic efficiency among plants by examining the pseudo-color images of chlorophyll fluorescence's spatial distribution. This approach elucidates the discrepancies in photosynthetic efficiency across various regions. The plant photosynthetic phenotype imaging system affords an intuitive and comprehensive view of the photosynthetic efficiency in plants under diverse stress conditions. Additionally, It provides technical support for the analysis of the spatial heterogeneity of high-throughput photosynthetic efficiency in plants. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Three-Dimensional Environment Perception Technology for Agricultural Wheeled Robots: A Review CHEN Ruiyun, TIAN Wenbin, BAO Haibo, LI Duan, XIE Xinhao, ZHENG Yongjun, TAN Yu Smart Agriculture    2023, 5 (4): 16-32.   DOI: 10.12133/j.smartag.SA202308006 Abstract （744）   HTML （91）    PDF（pc） （1885KB）（1666）       Knowledge map    Save [Significance] As the research focus of future agricultural machinery, agricultural wheeled robots are developing in the direction of intelligence and multi-functionality. Advanced environmental perception technologies serve as a crucial foundation and key components to promote intelligent operations of agricultural wheeled robots. However, considering the non-structured and complex environments in agricultural on-field operational processes, the environmental information obtained through conventional 2D perception technologies is limited. Therefore, 3D environmental perception technologies are highlighted as they can provide more dimensional information such as depth, among others, thereby directly enhancing the precision and efficiency of unmanned agricultural machinery operation. This paper aims to provide a detailed analysis and summary of 3D environmental perception technologies, investigate the issues in the development of agricultural environmental perception technologies, and clarify the future key development directions of 3D environmental perception technologies regarding agricultural machinery, especially the agricultural wheeled robot. [Progress] Firstly, an overview of the general status of wheeled robots was introduced, considering their dominant influence in environmental perception technologies. It was concluded that multi-wheel robots, especially four-wheel robots, were more suitable for the agricultural environment due to their favorable adaptability and robustness in various agricultural scenarios. In recent years, multi-wheel agricultural robots have gained widespread adoption and application globally. The further improvement of the universality, operation efficiency, and intelligence of agricultural wheeled robots is determined by the employed perception systems and control systems. Therefore, agricultural wheeled robots equipped with novel 3D environmental perception technologies can obtain high-dimensional environmental information, which is significant for improving the accuracy of decision-making and control. Moreover, it enables them to explore effective ways to address the challenges in intelligent environmental perception technology. Secondly, the recent development status of 3D environmental perception technologies in the agriculture field was briefly reviewed. Meanwhile, sensing equipment and the corresponding key technologies were also introduced. For the wheeled robots reported in the agriculture area, it was noted that the applied technologies of environmental perception, in terms of the primary employed sensor solutions, were divided into three categories: LiDAR, vision sensors, and multi-sensor fusion-based solutions. Multi-line LiDAR had better performance on many tasks when employing point cloud processing algorithms. Compared with LiDAR, depth cameras such as binocular cameras, TOF cameras, and structured light cameras have been comprehensively investigated for their application in agricultural robots. Depth camera-based perception systems have shown superiority in cost and providing abundant point cloud information. This study has investigated and summarized the latest research on 3D environmental perception technologies employed by wheeled robots in agricultural machinery. In the reported application scenarios of agricultural environmental perception, the state-of-the-art 3D environmental perception approaches have mainly focused on obstacle recognition, path recognition, and plant phenotyping. 3D environmental perception technologies have the potential to enhance the ability of agricultural robot systems to understand and adapt to the complex, unstructured agricultural environment. Furthermore, they can effectively address several challenges that traditional environmental perception technologies have struggled to overcome, such as partial sensor information loss, adverse weather conditions, and poor lighting conditions. Current research results have indicated that multi-sensor fusion-based 3D environmental perception systems outperform single-sensor-based systems. This superiority arises from the amalgamation of advantages from various sensors, which concurrently serve to mitigate individual shortcomings. [Conclusions and Prospects] The potential of 3D environmental perception technology for agricultural wheeled robots was discussed in light of the evolving demands of smart agriculture. Suggestions were made to improve sensor applicability, develop deep learning-based agricultural environmental perception technology, and explore intelligent high-speed online multi-sensor fusion strategies. Currently, the employed sensors in agricultural wheeled robots may not fully meet practical requirements, and the system's cost remains a barrier to widespread deployment of 3D environmental perception technologies in agriculture. Therefore, there is an urgent need to enhance the agricultural applicability of 3D sensors and reduce production costs. Deep learning methods were highlighted as a powerful tool for processing information obtained from 3D environmental perception sensors, improving response speed and accuracy. However, the limited datasets in the agriculture field remain a key issue that needs to be addressed. Additionally, multi-sensor fusion has been recognized for its potential to enhance perception performance in complex and changeable environments. As a result, it is clear that 3D environmental perception technology based on multi-sensor fusion is the future development direction of smart agriculture. To overcome challenges such as slow data processing speed, delayed processed data, and limited memory space for storing data, it is essential to investigate effective fusion schemes to achieve online multi-source information fusion with greater intelligence and speed. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agri-QA Net: Multimodal Fusion Large Language Model Architecture for Crop Knowledge Question-Answering System WU Huarui, ZHAO Chunjiang, LI Jingchen Smart Agriculture    2025, 7 (1): 1-10.   DOI: 10.12133/j.smartag.SA202411005 Abstract （1214）   HTML （163）    PDF（pc） （1010KB）（1659）       Knowledge map    Save [Objective] As agriculture increasingly relies on technological innovations to boost productivity and ensure sustainability, farmers need efficient and accurate tools to aid their decision-making processes. A key challenge in this context is the retrieval of specialized agricultural knowledge, which can be complex and diverse in nature. Traditional agricultural knowledge retrieval systems have often been limited by the modalities they utilize (e.g., text or images alone), which restricts their effectiveness in addressing the wide range of queries farmers face. To address this challenge, a specialized multimodal question-answering system tailored for cabbage cultivation was proposed. The system, named Agri-QA Net, integrates multimodal data to enhance the accuracy and applicability of agricultural knowledge retrieval. By incorporating diverse data modalities, Agri-QA Net aims to provide a holistic approach to agricultural knowledge retrieval, enabling farmers to interact with the system using multiple types of input, ranging from spoken queries to images of crop conditions. By doing so, it helps address the complexity of real-world agricultural environments and improves the accessibility of relevant information. [Methods] The architecture of Agri-QA Net was built upon the integration of multiple data modalities, including textual, auditory, and visual data. This multifaceted approach enables the system to develop a comprehensive understanding of agricultural knowledge, allowed the system to learn from a wide array of sources, enhancing its robustness and generalizability. The system incorporated state-of-the-art deep learning models, each designed to handle one specific type of data. Bidirectional Encoder Representations from Transformers (BERT)'s bidirectional attention mechanism allowed the model to understand the context of each word in a given sentence, significantly improving its ability to comprehend complex agricultural terminology and specialized concepts. The system also incorporated acoustic models for processing audio inputs. These models analyzed the spoken queries from farmers, allowing the system to understand natural language inputs even in noisy, non-ideal environments, which was a common challenge in real-world agricultural settings. Additionally, convolutional neural networks (CNNs) were employed to process images from various stages of cabbage growth. CNNs were highly effective in capturing spatial hierarchies in images, making them well-suited for tasks such as identifying pests, diseases, or growth abnormalities in cabbage crops. These features were subsequently fused in a Transformer-based fusion layer, which served as the core of the Agri-QA Net architecture. The fusion process ensured that each modality—text, audio, and image—contributes effectively to the final model's understanding of a given query. This allowed the system to provide more nuanced answers to complex agricultural questions, such as identifying specific crop diseases or determining the optimal irrigation schedules for cabbage crops. In addition to the fusion layer, cross-modal attention mechanisms and domain-adaptive techniques were incorporated to refine the model's ability to understand and apply specialized agricultural knowledge. The cross-modal attention mechanism facilitated dynamic interactions between the text, audio, and image data, ensuring that the model paid attention to the most relevant features from each modality. Domain-adaptive techniques further enhanced the system's performance by tailoring it to specific agricultural contexts, such as cabbage farming, pest control, or irrigation management. [Results and Discussions] The experimental evaluations demonstrated that Agri-QA Net outperforms traditional single-modal or simple multimodal models in agricultural knowledge tasks. With the support of multimodal inputs, the system achieved an accuracy rate of 89.5%, a precision rate of 87.9%, a recall rate of 91.3%, and an F1-Score of 89.6%, all of which are significantly higher than those of single-modality models. The integration of multimodal data significantly enhanced the system's capacity to understand complex agricultural queries, providing more precise and context-aware answers. The addition of cross-modal attention mechanisms enabled for more nuanced and dynamic interaction between the text, audio, and image data, which in turn improved the model's understanding of ambiguous or context-dependent queries, such as disease diagnosis or crop management. Furthermore, the domain-adaptive technique enabled the system to focus on specific agricultural terminology and concepts, thereby enhancing its performance in specialized tasks like cabbage cultivation and pest control. The case studies presented further validated the system's ability to assist farmers by providing actionable, domain-specific answers to questions, demonstrating its practical application in real-world agricultural scenarios. [Conclusions] The proposed Agri-QA Net framework is an effective solution for addressing agricultural knowledge questions, especially in the domain of cabbage cultivation. By integrating multimodal data and leveraging advanced deep learning techniques, the system demonstrates a high level of accuracy and adaptability. This study not only highlights the potential of multimodal fusion in agriculture but also paves the way for future developments in intelligent systems designed to support precision farming. Further work will focus on enhancing the model's performance by expanding the dataset to include more diverse agricultural scenarios, refining the handling of dialectical variations in audio inputs, and improving the system's ability to detect rare crop diseases. The ultimate goal is to contribute to the modernization of agricultural practices, offering farmers more reliable and effective tools to solve the challenges in crop management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Chilli-YOLO: An Intelligent Maturity Detection Algorithm for Field-Grown Chilli Based on Improved YOLOv10 SI Chaoguo, LIU Mengchen, WU Huarui, MIAO Yisheng, ZHAO Chunjiang Smart Agriculture    2025, 7 (2): 160-171.   DOI: 10.12133/j.smartag.SA202411002 Abstract （327）   HTML （58）    PDF（pc） （2545KB）（1624）       Knowledge map    Save [Objective] In modern agriculture, the rapid and accurate detection of chillies at different maturity stages is a critical step for determining the optimal harvesting time and achieving intelligent sorting of field-grown chillies. However, existing target detection models face challenges in efficiency and accuracy when applied to the task of detecting chilli maturity, which limit their widespread use and effectiveness in practical applications. To address these challenges, a new algorithm, Chilli-YOLO, was proposed for achieving efficient and precise detection of chilli maturity in complex environments. [Methods] A comprehensive image dataset was collected, capturing chillis under diverse and realistic agricultural conditions, including varying lighting conditions, camera angles, and background complexities. These images were then meticulously categorized into four distinct maturity stages: Immature, transitional, mature, and dried. Data augmentation techniques were employed to expand the dataset and enhance the model's generalization capabilities. To develop an accurate and efficient chili maturity detection system, the YOLOv10s object detection network was chosen as the foundational architecture. The model's performance was further enhanced through strategic optimizations targeting the backbone network. Specifically, standard convolutional layers were replaced with Ghost convolutions. This technique generated more feature maps from fewer parameters, resulting in significant computational savings and improved processing speed without compromising feature extraction quality. Additionally, the C2f module was substituted with the more computationally efficient GhostConv module, further reducing redundancy and enhancing the model's overall efficiency. To improve the model's ability to discern subtle visual cues indicative of maturity, particularly in challenging scenarios involving occlusion, uneven lighting, or complex backgrounds, the partial self-attention (PSA) module within YOLOv10s was replaced with the second-order channel attention (SOCA) mechanism. SOCA leverages higher-order feature correlations to more effectively capture fine-grained characteristics of the chillis. This enabled the model to focus on relevant feature channels and effectively identify subtle maturity-related features, even when faced with significant visual noise and interference. Finally, to refine the precision of target localization and minimize bounding box errors, the extended intersection over union (XIoU) loss function was integrated into the model training process. XIoU enhances the traditional IoU loss by considering factors such as the aspect ratio difference and the normalized distance between the predicted and ground truth bounding boxes. By optimizing for these factors, the model achieved significantly improved localization accuracy, resulting in a more precise delineation of chillis in the images and contributing to the overall enhancement of the detection performance. The combined implementation of these improvements aimed to construct an effective approach to correctly classify the maturity level of chillis within the challenging and complex environment of a real-world farm. [Results and Discussion] The experimental results on the custom-built chilli maturity detection dataset showed that the Chilli-YOLO model performed excellently across multiple evaluation metrics. The model achieved an accuracy of 90.7%, a recall rate of 82.4%, and a mean average precision (mAP) of 88.9%. Additionally, the model's computational load, parameter count, model size, and inference time were 18.3 GFLOPs, 6.37 M, 12.6 M, and 7.3 ms, respectively. Compared to the baseline model, Chilli-YOLO improved accuracy by 2.6 percent point, recall by 2.8 percent point and mAP by 2.8 percent point. At the same time, the model's computational load decreased by 6.2 GFLOPs, the parameter count decreased by 1.67 M, model size reduced by 3.9 M. These results indicated that Chilli-YOLO strikes a good balance between accuracy and efficiency, making it capable of fast and precise detection of chilli maturity in complex agricultural environments. Moreover, compared to earlier versions of the YOLO model, Chilli-YOLO showed improvements in accuracy of 2.7, 4.8, and 5 percent point over YOLOv5s, YOLOv8n, and YOLOv9s, respectively. Recall rates were higher by 1.1, 0.3, and 2.3 percent point, and mAP increased by 1.2, 1.7, and 2.3 percent point, respectively. In terms of parameter count, model size, and inference time, Chilli-YOLO outperformed YOLOv5. This avoided the issue of YOLOv8n's lower accuracy, which was unable to meet the precise detection needs of complex outdoor environments. When compared to the traditional two-stage network Faster RCNN, Chilli-YOLO showed significant improvements across all evaluation metrics. Additionally, compared to the one-stage network SSD, Chilli-YOLO achieved substantial gains in accuracy, recall, and mAP, with increases of 16.6%, 12.1%, and 16.8%, respectively. Chilli-YOLO also demonstrated remarkable improvements in memory usage, model size, and inference time. These results highlighted the superior overall performance of the Chilli-YOLO model in terms of both memory consumption and detection accuracy, confirming its advantages for chilli maturity detection. [Conclusions] The proposed Chilli-YOLO model optimizes the network structure and loss functions, not only can significantly improve detection accuracy but also effectively reduce computational overhead, making it better suites for resource-constrained agricultural production environments. The research provides a reliable technical reference for intelligent harvesting of chillies in agricultural production environments, especially in resource-constrained settings. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Vegetable Crop Growth Modeling in Digital Twin Platform Based on Large Language Model Inference ZHAO Chunjiang, LI Jingchen, WU Huarui, YANG Yusen Smart Agriculture    2024, 6 (6): 63-71.   DOI: 10.12133/j.smartag.SA202410008 Abstract （1733）   HTML （179）    PDF（pc） （1460KB）（1611）       Knowledge map    Save [Objective] In the era of digital agriculture, real-time monitoring and predictive modeling of crop growth are paramount, especially in autonomous farming systems. Traditional crop growth models, often constrained by their reliance on static, rule-based methods, fail to capture the dynamic and multifactorial nature of vegetable crop growth. This research tried to address these challenges by leveraging the advanced reasoning capabilities of pre-trained large language models (LLMs) to simulate and predict vegetable crop growth with accuracy and reliability. Modeling the growth of vegetable crops within these platforms has historically been hindered by the complex interactions among biotic and abiotic factors. [Methods] The methodology was structured in several distinct phases. Initially, a comprehensive dataset was curated to include extensive information on vegetable crop growth cycles, environmental conditions, and management practices. This dataset incorporates continuous data streams such as soil moisture, nutrient levels, climate variables, pest occurrence, and historical growth records. By combining these data sources, the study ensured that the model was well-equipped to understand and infer the complex interdependencies inherent in crop growth processes. Then, advanced techniques was emploied for pre-training and fine-tuning LLMs to adapt them to the domain-specific requirements of vegetable crop modeling. A staged intelligent agent ensemble was designed to work within the digital twin platform, consisting of a central managerial agent and multiple stage-specific agents. The managerial agent was responsible for identifying transitions between distinct growth stages of the crops, while the stage-specific agents were tailored to handle the unique characteristics of each growth phase. This modular architecture enhanced the model's adaptability and precision, ensuring that each phase of growth received specialized attention and analysis. [Results and Discussions] The experimental validation of this method was conducted in a controlled agricultural setting at the Xiaotangshan Modern Agricultural Demonstration Park in Beijing. Cabbage (Zhonggan 21) was selected as the test crop due to its significance in agricultural production and the availability of comprehensive historical growth data. Over five years, the dataset collected included 4 300 detailed records, documenting parameters such as plant height, leaf count, soil conditions, irrigation schedules, fertilization practices, and pest management interventions. This dataset was used to train the LLM-based system and evaluate its performance using ten-fold cross-validation. The results of the experiments demonstrating the efficacy of the proposed system in addressing the complexities of vegetable crop growth modeling. The LLM-based model achieved 98% accuracy in predicting crop growth degrees and a 99.7% accuracy in identifying growth stages. These metrics significantly outperform traditional machine learning approaches, including long short-term memory (LSTM), XGBoost, and LightGBM models. The superior performance of the LLM-based system highlights its ability to reason over heterogeneous data inputs and make precise predictions, setting a new benchmark for crop modeling technologies. Beyond accuracy, the LLM-powered system also excels in its ability to simulate growth trajectories over extended periods, enabling farmers and agricultural managers to anticipate potential challenges and make proactive decisions. For example, by integrating real-time sensor data with historical patterns, the system can predict how changes in irrigation or fertilization practices will impact crop health and yield. This predictive capability is invaluable for optimizing resource allocation and mitigating risks associated with climate variability and pest outbreaks. [Conclusions] The study emphasizes the importance of high-quality data in achieving reliable and generalizable models. The comprehensive dataset used in this research not only captures the nuances of cabbage growth but also provides a blueprint for extending the model to other crops. In conclusion, this research demonstrates the transformative potential of combining large language models with digital twin technology for vegetable crop growth modeling. By addressing the limitations of traditional modeling approaches and harnessing the advanced reasoning capabilities of LLMs, the proposed system sets a new standard for precision agriculture. Several avenues also are proposed for future work, including expanding the dataset, refining the model architecture, and developing multi-crop and multi-region capabilities. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Progressive Convolutional Net Based Method for Agricultural Named Entity Recognition JI Jie, JIN Zhou, WANG Rujing, LIU Haiyan, LI Zhiyuan Smart Agriculture    2023, 5 (1): 122-131.   DOI: 10.12133/j.smartag.SA202303001 Abstract （1000）   HTML （42）    PDF（pc） （965KB）（1608）       Knowledge map    Save Pre-training refers to the process of training deep neural network parameters on a large corpus before a specific task model performs a particular task. This approach enables downstream tasks to fine-tune the pre-trained model parameters based on a small amount of labeled data, eliminating the need to train a new model from scratch. Currently, research on named entity recognition (NER) using pre-trained language model (PLM) only uses the last layer of the PLM to express output when facing challenges such as complex entity naming methods and fuzzy entity boundaries in the agricultural field. This approach ignores the rich information contained in the internal layers of the model themselves. To address these issues, a named entity recognition method based on progressive convolutional networks has been proposed. This method stores natural sentences and outputs representations of each layer obtained through PLM. The intermediate outputs of the pre-trained model are sequentially convolved to extract shallow feature information that may have been overlooked previously. Using the progressive convolutional network module proposed in this research, the adjacent two-layer representations are convolved from the first layer, and the fusion result continues to be convolved with the next layer, resulting in enhanced sentence embedding that includes the entire information dimension of the model layer. The method does not require the introduction of external information, which makes the sentence representation contain richer information. Research has shown that the sentence embedding output of the model layer near the input contains more fine-grained information, such as phrases and phrases, which can assist with NER problems in the agricultural field. Fully utilizing the computational power already used, the results obtained can enhance the representation embedding of sentences. Finally, the conditional random field (CRF) model was used to generate the global optimal sequence. On a constructed agricultural dataset containing four types of agricultural entities, the proposed method's comprehensive indicator F1 value increased by 3.61% points compared to the basic BERT (Bidirectional Encoder Representation from Transformers) model. On the open dataset MSRA, the F1 value also increased to 94.96%, indicating that the progressive convolutional network can enhance the model's ability to represent natural language and has advantages in NER tasks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Localization Method for Agricultural Robots Based on Fusion of LiDAR and IMU LIU Yang, JI Jie, PAN Deng, ZHAO Lijun, LI Mingsheng Smart Agriculture    2024, 6 (3): 94-106.   DOI: 10.12133/j.smartag.SA202401009 Abstract （676）   HTML （58）    PDF（pc） （3329KB）（1590）       Knowledge map    Save [Objective] High-precision localization technology serves as the crucial foundation in enabling the autonomous navigation operations of intelligent agricultural robots. However, the traditional global navigation satellite system (GNSS) localization method faces numerous limitations, such as tree shadow, electromagnetic interference, and other factors in the agricultural environment brings challenges to the accuracy and reliability of localization technology. To address the deficiencies and achieve precise localization of agricultural robots independent of GNSS, a localization method was proposed based on the fusion of three-dimensional light detection and ranging (LiDAR) data and inertial measurement unit (IMU) information to enhance localization accuracy and reliability. [Methods] LiDAR was used to obtain point cloud data in the agricultural environment and realize self-localization via point cloud matching. By integrating real-time motion parameter measurements from the IMU with LiDAR data, a high-precision localization solution for agricultural robots was achieved through a specific fusion algorithm. Firstly, the LiDAR-obtained point cloud data was preprocessed and the depth map was used to save the data. This approach could reduce the dimensionality of the original LiDAR point cloud, and eliminate the disorder of the original LiDAR point cloud arrangement, facilitating traversal and clustering through graph search. Given the presence of numerous distinct crops like trees in the agricultural environment, an angle-based clustering method was adopted. Specific angle-based clustering criteria were set to group the point cloud data, leading to the segmentation of different clusters of points, and obvious crops in the agricultural environment was effectively perceived. Furthermore, to improve the accuracy and stability of positioning, an improved three-dimensional normal distribution transform (3D-NDT) localization algorithm was proposed. This algorithm operated by matching the LiDAR-scanned point cloud data in real time with the pre-existing down sampled point cloud map to achieve real-time localization. Considering that direct down sampling of LiDAR point clouds in the agricultural environment could result in the loss of crucial environmental data, a point cloud clustering operation was used in place of down sampling operation, thereby improving matching accuracy and positioning precision. Secondly, to address potential constraints and shortcomings of using a single sensor for robot localization, a multi-sensor information fusion strategy was deployed to improve the localization accuracy. Specifically, the extended Kalman filter algorithm (EKF) was chosen to fuse the localization data from LiDAR point cloud and the IMU odometer information. The IMU provided essential motion parameters such as acceleration and angular velocity of the agricultural robot, and by combining with the LiDAR-derived localization information, the localization of the agricultural robot could be more accurately estimated. This fusion approach maximized the advantages of different sensors, compensated for their individual limitations, and improved the overall localization accuracy of the agricultural robot. [Results and Discussions] A series of experimental results in the Gazebo simulation environment of the robot operating system (ROS) and real operation scenarios showed that the fusion localization method proposed had significant advantages. In the simulation environment, the average localization errors of the proposed multi-sensor data fusion localization method were 1.7 and 1.8 cm, respectively, while in the experimental scenario, these errors were 3.3 and 3.3 cm, respectively, which were significantly better than the traditional 3D-NDT localization algorithm. These findings showed that the localization method proposed in this study could achieve high-precision localization in the complex agricultural environment, and provide reliable localization assistance for the autonomous functioning of agricultural robots. [Conclusions] The proposed localization method based on the fusion of LiDAR data and IMU information provided a novel localization solution for the autonomous operation of agricultural robots in areas with limited GNSS reception. Through the comprehensive utilization of multi-sensor information and adopting advanced data processing and fusion algorithms, the localization accuracy of agricultural robots could be significantly improved, which could provide a new reference for the intelligence and automation of agricultural production. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Knowledge Recommendation Model Integrating Time Perception and Context Filtering WANG Pengzhe, ZHU Huaji, MIAO Yisheng, LIU Chang, WU Huarui Smart Agriculture    2024, 6 (1): 123-134.   DOI: 10.12133/j.smartag.SA202312012 Abstract （899）   HTML （35）    PDF（pc） （1503KB）（1585）       Knowledge map    Save Objective Knowledge services in agricultural scenarios have the characteristics of long periodicity and prolonged activity time. Traditional recommendation models cannot effectively mine hidden information in agricultural scenarios, in order to improve the quality of agricultural knowledge recommendation services, agricultural contextual information based on agricultural time should be fully considered. To address these issues, a Time-aware and filter-enhanced sequential recommendation model for agricultural knowledge (TiFSA) was proposed, integrating temporal perception and enhanced filtering. Methods First, based on the temporal positional embedding, combining the temporal information of farmers' interactions with positional embedding based on time perception, it helped to learn project relevance based on agricultural season in agricultural contexts. A multi-head self-attention network recommendation algorithm based on time-awareness was proposed for the agricultural knowledge recommendation task, which extracted different interaction time information in the user interaction sequence and introduced it into the multi-head self-attention network to calculate the attention weight, which encoded the user's periodic interaction information based on the agricultural time, and also effectively captured the user's dynamic preference information over time. Then, through the temporal positional embedding, a filter filtering algorithm was introduced to adaptively attenuate the noise in farmers' situational data adaptively. The filtering algorithm was introduced to enhance the filtering module to effectively filter the noisy information in the agricultural dataset and alleviate the overfitting problem due to the poorly normalized and sparse agricultural dataset. By endowing the model with lower time complexity and adaptive noise attenuation capability. The applicability of this method in agricultural scenarios was improved. Next, a multi-head self attention network with temporal information was constructed to achieve unified modeling of time, projects, and features, and represent farmers' preferences of farmers over time in context, thereby providing reliable recommendation results for users. Finally, the AdamW optimizer was used to update and compute the model parameters. AdamW added L2 regularization and an appropriate penalty mechanism for larger weights, which could update all weights more smoothly and alleviate the problem of falling into local minima. Applied in the field of agricultural recommendation, it could further improve the training effect of the model. The experimental data came from user likes, comments, and corresponding time information in the "National Agricultural Knowledge Intelligent Service Cloud Platform", and the dataset ml-1m in the movie recommendation scenario was selected as an auxiliary validation of the performance of this model. Results and Discussions According to the user interaction sequence datasets in the "National Agricultural Knowledge Intelligent Service Cloud Platform", from the experimental results, it could be learned that TiFSA outperforms the other models on two different datasets, in which the enhancement was more obvious on the Agriculture dataset, where HR and NDCG were improved by 14.02% and 16.19%, respectively, compared to the suboptimal model, TiSASRec; while on the ml-1m dataset compared to the suboptimal model, SASRec, HR and NDCG were improved by 1.90% and 2.30%, respectively. In summary, the TiFSA model proposed in this paper has a large improvement compared with other models, which verifies verified the effectiveness of the TiFSA model and showed that the time interval information of farmer interaction and the filtering algorithm play an important role in the improvement of the model performance in the agricultural context. From the results of the ablation experiments, it could be seen that when the time-aware and enhanced filtering modules were removed, the values of the two metrics HR@10 and NDCG@10 were 0.293 6 and 0.203 9, respectively, and the recommended performance was poor. When only the time-aware module and only the augmentation filtering module were removed, the experimental results had different degrees of improvement compared to TiFSA-tf, and the TiFSA model proposed in this paper achieved the optimal performance in the two evaluation metrics. When only the multi-head self-attention network was utilized for recommendation, both recommendation metrics of the model were lower, indicating that the traditional sequence recommendation method that only considered the item number was not applicable to agricultural scenarios. When the augmented filtering module was introduced without the time-aware module, the model performance was improved, but still failed to achieve the ideal recommendation effect. When only the time-aware module was introduced without the augmented filtering module, there was a significant improvement in the model effect, which proved that the time-aware module was more applicable to agricultural scenarios and can effectively improve the model performance of the sequence recommendation task. When both time-aware and augmented filtering modules were introduced, the model performance was further improved, which on the one hand illustrated the dependence of the augmented filtering module on the time-aware module, and on the other hand verified the necessity of adopting the augmented filtering to the time-aware self-attention network model. Conclusions This research proposes an agricultural knowledge recommendation model that integrates time-awareness and augmented filtering, which introduces the user's interaction time interval into the embedded information, so that the model effectively learns the information of agricultural time in the agricultural scene, and the prediction of the user's interaction time and the object is more closely related to the actual scene; augmented filtering algorithms are used to attenuate the noise in the agricultural data. At the same time, the enhanced filtering algorithm is used to attenuate the noise in the agricultural data, and can be effectively integrated into the model for use, further improving the recommendation performance of the model. The experimental results show the effectiveness of the proposed TiFSA model on the agricultural dataset. The ablation experiments confirm the positive effect of time-awareness and enhanced filtering modules on the improvement of recommendation performance. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Remote Sensing Extraction Method of Terraced Fields Based on Improved DeepLab v3+ ZHANG Jun, CHEN Yuyan, QIN Zhenyu, ZHANG Mengyao, ZHANG Jun Smart Agriculture    2024, 6 (3): 46-57.   DOI: 10.12133/j.smartag.SA202312028 Abstract （574）   HTML （44）    PDF（pc） （2806KB）（1562）       Knowledge map    Save [Objective] The accurate estimation of terraced field areas is crucial for addressing issues such as slope erosion control, water retention, soil conservation, and increasing food production. The use of high-resolution remote sensing imagery for terraced field information extraction holds significant importance in these aspects. However, as imaging sensor technologies continue to advance, traditional methods focusing on shallow features may no longer be sufficient for precise and efficient extraction in complex terrains and environments. Deep learning techniques offer a promising solution for accurately extracting terraced field areas from high-resolution remote sensing imagery. By utilizing these advanced algorithms, detailed terraced field characteristics with higher levels of automation can be better identified and analyzed. The aim of this research is to explore a proper deep learning algorithm for accurate terraced field area extraction in high-resolution remote sensing imagery. [Methods] Firstly, a terraced dataset was created using high-resolution remote sensing images captured by the Gaofen-6 satellite during fallow periods. The dataset construction process involved data preprocessing, sample annotation, sample cropping, and dataset partitioning with training set augmentation. To ensure a comprehensive representation of terraced field morphologies, 14 typical regions were selected as training areas based on the topographical distribution characteristics of Yuanyang county. To address misclassifications near image edges caused by limited contextual information, a sliding window approach with a size of 256 pixels and a stride of 192 pixels in each direction was utilized to vary the positions of terraced fields in the images. Additionally, geometric augmentation techniques were applied to both images and labels to enhance data diversity, resulting in a high-resolution terraced remote sensing dataset. Secondly, an improved DeepLab v3+ model was proposed. In the encoder section, a lightweight MobileNet v2 was utilized instead of Xception as the backbone network for the semantic segmentation model. Two shallow features from the 4th and 7th layers of the MobileNet v2 network were extracted to capture relevant information. To address the need for local details and global context simultaneously, the multi-scale feature fusion (MSFF) module was employed to replace the atrous spatial pyramid pooling (ASPP) module. The MSFF module utilized a series of dilated convolutions with increasing dilation rates to handle information loss. Furthermore, a coordinate attention mechanism was applied to both shallow and deep features to enhance the network's understanding of targets. This design aimed to lightweight the DeepLab v3+ model while maintaining segmentation accuracy, thus improving its efficiency for practical applications. [Results and Discussions] The research findings reveal the following key points: (1) The model trained using a combination of near-infrared, red, and green (NirRG) bands demonstrated the optimal overall performance, achieving precision, recall, F1-Score, and intersection over union (IoU) values of 90.11%, 90.22%, 90.17% and 82.10%, respectively. The classification results indicated higher accuracy and fewer discrepancies, with an error in reference area of only 12 hm2. (2) Spatial distribution patterns of terraced fields in Yuanyang county were identified through the deep learning model. The majority of terraced fields were found within the slope range of 8º to 25º, covering 84.97% of the total terraced area. Additionally, there was a noticeable concentration of terraced fields within the altitude range of 1 000 m to 2 000 m, accounting for 95.02% of the total terraced area. (3) A comparison with the original DeepLab v3+ network showed that the improved DeepLab v3+ model exhibited enhancements in terms of precision, recall, F1-Score, and IoU by 4.62%, 2.61%, 3.81% and 2.81%, respectively. Furthermore, the improved DeepLab v3+ outperformed UNet and the original DeepLab v3+ in terms of parameter count and floating-point operations. Its parameter count was only 28.6% of UNet and 19.5% of the original DeepLab v3+, while the floating-point operations were only 1/5 of UNet and DeepLab v3+. This not only improved computational efficiency but also made the enhanced model more suitable for resource-limited or computationally less powerful environments. The lightweighting of the DeepLab v3+ network led to improvements in accuracy and speed. However, the slection of the NirGB band combination during fallow periods significantly impacted the model's generalization ability. [Conclusions] The research findings highlights the significant contribution of the near-infrared (NIR) band in enhancing the model's ability to learn terraced field features. Comparing different band combinations, it was evident that the NirRG combination resulted in the highest overall recognition performance and precision metrics for terraced fields. In contrast to PSPNet, UNet, and the original DeepLab v3+, the proposed model showcased superior accuracy and performance on the terraced field dataset. Noteworthy improvements were observed in the total parameter count, floating-point operations, and the Epoch that led to optimal model performance, outperforming UNet and DeepLab v3+. This study underscores the heightened accuracy of deep learning in identifying terraced fields from high-resolution remote sensing imagery, providing valuable insights for enhanced monitoring and management of terraced landscapes. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Severity Grading Model for Camellia Oleifera Anthracnose Infection Based on Improved YOLACT NIE Ganggang, RAO Honghui, LI Zefeng, LIU Muhua Smart Agriculture    2024, 6 (3): 138-147.   DOI: 10.12133/j.smartag.SA202402002 Abstract （417）   HTML （34）    PDF（pc） （2130KB）（1553）       Knowledge map    Save [Objective] Camellia oleifera is one of the four major woody oil plants in the world. Diseases is a significant factor leading to the decline in quality of Camellia oleifera and the financial loss of farmers. Among these diseases, anthracnose is a common and severe disease in Camellia oleifera forests, directly impacting yields and production rates. Accurate disease assessment can improve the prevention and control efficiency and safeguarding the farmers' profit. In this study, an improved You Only Look at CoefficienTs (YOLACT) based method was proposed to realize automatic and efficient grading of the severity of Camellia oleifera leaf anthracnose. [Methods] High-resolution images of Camellia oleifera anthracnose leaves were collected using a smartphone at the National Camellia oleifera Seed Base of Jiangxi Academy of Forestry, and finally 975 valid images were retained after a rigorous screening process. Five data enhancement means were applied, and a data set of 5 850 images was constructed finally, which was divided into training, validation, and test sets in a ratio of 7:2:1. For model selection, the Camellia-YOLACT model was proposed based on the YOLACT instance segmentation model, and by introducing improvements such as Swin-Transformer, weighted bi-directional feature pyramid network, and HardSwish activation function. The Swin Transformer was utilized for feature extraction in the backbone network part of YOLACT, leveraging the global receptive field and shift window properties of the self-attention mechanism in the Transformer architecture to enhance feature extraction capabilities. Additionally, a weighted bidirectional feature pyramid network was introduced to fuse feature information from different scales to improve the detection ability of the model for objects at different scales, thereby improving the detection accuracy. Furthermore, to increase the the model's robustness against the noise in the input data, the HardSwish activation function with stronger nonlinear capability was adopted to replace the ReLu activation function of the original model. Since images in natural environments usually have complex background and foreground information, the robustness of HardSwish helped the model better handling these situations and further improving the detection accuracy. With the above improvements, the Camellia-YOLACT model was constructed and experimentally validated by testing the Camellia oleifera anthracnose leaf image dataset. [Results and Discussions] A transfer learning approach was used for experimental validation on the Camellia oleifera anthracnose severity grading dataset, and the results of the ablation experiments showed that the mAP75 of Camellia-YOLACT proposed in this study was 86.8%, mAPall was 78.3%, mAR was 91.6% which were 5.7%, 2.5% and 7.9% higher than YOLACT model. In the comparison experiments, Camellia-YOLACT performed better than Segmenting Objects by Locations (SOLO) in terms of both accuracy and speed, and its detection speed was doubled compared to Mask R-CNN algorithm. Therefore, the Camellia-YOLACT algorithm was suitable in Camellia oleifera gardens for anthracnose real-time segmentation. In order to verify the outdoors detection performance of Camellia-YOLACT model, 36 groups of Camellia oleifera anthracnose grading experiments were conducted. Experimental results showed that the grading correctness of Camellia oleifera anthracnose injection severity reached 94.4%, and the average absolute error of K-value was 1.09%. Therefore, the Camellia-YOLACT model proposed in this study has a better performance on the grading of the severity of Camellia oleifera anthracnose. [Conclusions] The Camellia-YOLACT model proposed got high accuracy in leaf and anthracnose segmentation of Camellia oleifera, on the basis of which it can realize automatic grading of the severity of Camellia oleifera anthracnose. This research could provide technical support for the precise control of Camellia oleifera diseases. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Capacitive Soil Moisture Sensor Based on MoS2 LI Lu, GE Yuqing, ZHAO Jianlong Smart Agriculture    2024, 6 (1): 28-35.   DOI: 10.12133/j.smartag.SA202309020 Abstract （499）   HTML （38）    PDF（pc） （1373KB）（1435）       Knowledge map    Save Objective The soil moisture content is a crucial factor that directly affected the growth and yield of crops. By using a soil measurement instrument to measure the soil's moisture content, lots of powerful data support for the development of agriculture can be provided. Furthermore, these data have guiding significance for the implementation of scientific irrigation and water-saving irrigation in farmland. In order to develop a reliable and efficient soil moisture sensor, a new capacitive soil moisture sensor based on microfabrication technology was proposed in this study. Capacitive moisture sensors have the advantages of low power consumption, good performance, long-term stability, and easy industrialization. Method The forked electrode array consists of multiple capacitors connected in parallel on the same plane. The ideal design parameters of 10 μm spacing and 75 pairs of forked electrodes were obtained by calculating the design of forked finger logarithms, forked finger spacing, forked finger width, forked finger length, and electrode thickness, and studying the influence of electrode parameters on capacitance sensitivity using COMSOL Multiphysics software. The size obtained an initial capacitance on the order of picofarads, and was not easily breakdown or failed. The sensor was constructed using microelectromechanical systems (MEMS) technology, where a 30 nm titanium adhesion layer was sputtered onto a glass substrate, followed by sputtering a 100 nm gold electrode to form a symmetrical structure of forked electrodes. Due to the strong adsorption capacity of water molecules of the MoS2 (molybdenum disulfide) layer, it exhibited high sensitivity to soil moisture and demonstrated excellent soil moisture sensing performance. The molybdenum disulfide was coated onto the completed electrodes as the humidity-sensitive material to create a humidity sensing layer. When the humidity changed, the dielectric constant of the electrode varied due to the moisture-absorbing characteristics of molybdenum disulfide, and the capacitance value of the device changed accordingly, thus enabling the measurement of soil moisture. Subsequently, the electrode was encapsulated with a polytetrafluoroethylene (PTFE) polymer film. The electrode encapsulated with the microporous film could be directly placed in the soil, which avoided direct contact between the soil/sand particles and the molybdenum disulfide on the device and allowed the humidity sensing unit to only capture the moisture in the soil for measuring humidity. This ensured the device's sensitivity to water moisture and improved its long-term stability. The method greatly reduced the size of the sensor, making it an ideal choice for on-site dynamic monitoring of soil moisture. Results and Discussions The surface morphology of molybdenum disulfide was characterized and analyzed using a Scanning Electron Microscope (SEM). It was observed that molybdenum disulfide nanomaterial exhibited a sheet-like two-dimensional structure, with smooth surfaces on the nanosheets. Some nanosheets displayed sharp edges or irregular shapes along the edges, and they were irregularly arranged with numerous gaps in between. The capacitive soil moisture sensor, which utilized molybdenum disulfide as the humidity-sensitive layer, exhibited excellent performance under varying levels of environmental humidity and soil moisture. At room temperature, a humidity generator was constructed using saturated salt solutions. Saturated solutions of lithium chloride, potassium acetate, magnesium chloride, copper chloride, sodium chloride, potassium chloride, and potassium sulfate were used to generate relative humidity levels of 11%, 23%, 33%, 66%, 75%, 84%, and 96%, respectively. The capacitance values of the sensor were measured at different humidity levels using an LCR meter (Agilent E4980A). The capacitance output of the sensor at a frequency of 200 Hz ranged from 12.13 pF to 187.42 nF as the relative humidity varied between 11% to 96%. The sensor exhibited high sensitivity and a wide humidity sensing range. Additionally, the frequency of the input voltage signal had a significant impact on the capacitance output of the sensor. As the testing frequency increased, the response of the sensor's system decreased. The humidity sensing performance of the sensor was tested in soil samples with moisture content of 8.66%, 13.91%, 22.02%, 31.11%, and 42.75%, respectively. As the moisture content in the soil increased from 8.66% to 42.75%, the capacitance output of the sensor at a frequency of 200 Hz increased from 119.51 nF to 377.98 nF, demonstrating a relatively high sensitivity. Similarly, as the frequency of the input voltage increased, the capacitance output of the sensor decreased. Additionally, the electrode exhibited good repeatability and the sensitivity of the sensor increased significantly as the testing frequency decreased. Conclusions The capacitive soil moisture sensor holds promise for effective and accurate monitoring of soil moisture levels, with its excellent performance, sensitivity, repeatability, and responsiveness to changes in humidity and soil moisture. The ultimate goal of this study is to achieve long-term monitoring of capacitance changes in capacitive soil moisture sensors, enabling monitoring of long-term changes in soil moisture. This will enable farmers to optimize irrigation systems, improve crop yields, and reduce water usage. In conclusion, the development of this innovative soil moisture sensor has the potential to promote agricultural modernization by providing accurate and reliable monitoring of soil moisture levels. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Image Segmentation Method Combined with VoVNetv2 and Shuffle Attention Mechanism for Fish Feeding in Aquaculture WANG Herong, CHEN Yingyi, CHAI Yingqian, XU Ling, YU Huihui Smart Agriculture    2023, 5 (4): 137-149.   DOI: 10.12133/j.smartag.SA202310003 Abstract （430）   HTML （25）    PDF（pc） （2425KB）（1431）       Knowledge map    Save [Objective] Intelligent feeding methods are significant for improving breeding efficiency and reducing water quality pollution in current aquaculture. Feeding image segmentation of fish schools is a critical step in extracting the distribution characteristics of fish schools and quantifying their feeding behavior for intelligent feeding method development. While, an applicable approach is lacking due to images challenges caused by blurred boundaries and similar individuals in practical aquaculture environment. In this study, a high-precision segmentation method was proposed for fish school feeding images and provides technical support for the quantitative analysis of fish school feeding behavior. [Methods] The novel proposed method for fish school feeding images segmentation combined VoVNetv2 with an attention mechanism named Shuffle Attention. Firstly, a fish feeding segmentation dataset was presented. The dataset was collected at the intensive aquaculture base of Laizhou Mingbo Company in Shandong province, with a focus on Oplegnathus punctatus as the research target. Cameras were used to capture videos of the fish school before, during, and after feeding. The images were annotated at the pixel level using Labelme software. According to the distribution characteristics of fish feeding and non-feeding stage, the data was classified into two semantic categories— non-occlusion and non-aggregation fish (fish1) and occlusion or aggregation fish (fish2). In the preprocessing stage, data cleaning and image augmentation were employed to further enhance the quality and diversity of the dataset. Initially, data cleaning rules were established based on the distribution of annotated areas within the dataset. Images with outlier annotations were removed, resulting in an improvement in the overall quality of the dataset. Subsequently, to prevent the risk of overfitting, five data augmentation techniques (random translation, random flip, brightness variation, random noise injection, random point addition) were applied for mixed augmentation on the dataset, contributing to an increased diversity of the dataset. Through data augmentation operations, the dataset was expanded to three times its original size. Eventually, the dataset was divided into a training dataset and testing dataset at a ratio of 8:2. Thus, the final dataset consisted of 1 612 training images and 404 testing images. In detail, there were a total of 116 328 instances of fish1 and 20 924 instances of fish2. Secondly, a fish feeding image segmentation method was proposed. Specifically, VoVNetv2 was used as the backbone network for the Mask R-CNN model to extract image features. VoVNetv2 is a backbone network with strong computational capabilities. Its unique feature aggregation structure enables effective fusion of features at different levels, extracting diverse feature representations. This facilitates better capturing of fish schools of different sizes and shapes in fish feeding images, achieving accurate identification and segmentation of targets within the images. To maximize feature mappings with limited resources, the experiment replaced the channel attention mechanism in the one-shot aggregation (OSA) module of VoVNetv2 with a more lightweight and efficient attention mechanism named shuffle attention. This improvement allowed the network to concentrate more on the location of fish in the image, thus reducing the impact of irrelevant information, such as noise, on the segmentation results. Finally, experiments were conducted on the fish segmentation dataset to test the performance of the proposed method. [Results and Discussions] The results showed that the average segmentation accuracy of the Mask R-CNN network reached 63.218% after data cleaning, representing an improvement of 7.018% compared to the original dataset. With both data cleaning and augmentation, the network achieved an average segmentation accuracy of 67.284%, indicating an enhancement of 11.084% over the original dataset. Furthermore, there was an improvement of 4.066% compared to the accuracy of the dataset after cleaning alone. These results demonstrated that data preprocessing had a positive effect on improving the accuracy of image segmentation. The ablation experiments on the backbone network revealed that replacing the ResNet50 backbone with VoVNetv2-39 in Mask R-CNN led to a 2.511% improvement in model accuracy. After improving VoVNetv2 through the Shuffle Attention mechanism, the accuracy of the model was further improved by 1.219%. Simultaneously, the parameters of the model decreased by 7.9%, achieving a balance between accuracy and lightweight design. Comparing with the classic segmentation networks SOLOv2, BlendMask and CondInst, the proposed model achieved the highest segmentation accuracy across various target scales. For the fish feeding segmentation dataset, the average segmentation accuracy of the proposed model surpassed BlendMask, CondInst, and SOLOv2 by 3.982%, 12.068%, and 18.258%, respectively. Although the proposed method demonstrated effective segmentation of fish feeding images, it still exhibited certain limitations, such as omissive detection, error segmentation, and false classification. [Conclusions] The proposed instance segmentation algorithm (SA_VoVNetv2_RCNN) effectively achieved accurate segmentation of fish feeding images. It can be utilized for counting the number and pixel quantities of two types of fish in fish feeding videos, facilitating quantitative analysis of fish feeding behavior. Therefore, this technique can provide technical support for the analysis of piscine feeding actions. In future research, these issues will be addressed to further enhance the accuracy of fish feeding image segmentation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Diagnosis of Grapevine Leafroll Disease Severity Infection via UAV Remote Sensing and Deep Learning LIU Yixue, SONG Yuyang, CUI Ping, FANG Yulin, SU Baofeng Smart Agriculture    2023, 5 (3): 49-61.   DOI: 10.12133/j.smartag.SA202308013 Abstract （846）   HTML （125）    PDF（pc） （3044KB）（1403）       Knowledge map    Save [Objective] Wine grapes are severely affected by leafroll disease, which affects their growth, and reduces the quality of the color, taste, and flavor of wine. Timely and accurate diagnosis of leafroll disease severity is crucial for preventing and controlling the disease, improving the wine grape fruit quality and wine-making potential. Unmanned aerial vehicle (UAV) remote sensing technology provides high-resolution images of wine grape vineyards, which can capture the features of grapevine canopies with different levels of leafroll disease severity. Deep learning networks extract complex and high-level features from UAV remote sensing images and perform fine-grained classification of leafroll disease infection severity. However, the diagnosis of leafroll disease severity is challenging due to the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. [Method] A novel method for diagnosing leafroll disease severity was developed at a canopy scale using UAV remote sensing technology and deep learning. The main challenge of this task was the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. To address this challenge, a method that combined deep learning fine-grained classification and generative adversarial networks (GANs) was proposed. In the first stage, the GANformer, a Transformer-based GAN model was used, to generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity. To further analyze the image generation effect of GANformer. The t-distributed stochastic neighbor embedding (t-SNE) to visualize the learned features of real and simulated images. In the second stage, the CA-Swin Transformer, an improved image classification model based on the Swin Transformer and channel attention mechanism was used, to classify the patch images into different classes of leafroll disease infection severity. CA-Swin Transformer could also use a self-attention mechanism to capture the long-range dependencies of image patches and enhance the feature representation of the Swin Transformer model by adding a channel attention mechanism after each Transformer layer. The channel attention (CA) mechanism consisted of two fully connected layers and an activation function, which could extract correlations between different channels and amplify the informative features. The ArcFace loss function and instance normalization layer was also used to enhance the fine-grained feature extraction and downsampling ability for grapevine canopy images. The UAV images of wine grape vineyards were collected and processed into orthomosaic images. They labeled into three categories: healthy, moderate infection, and severe infection using the in-field survey data. A sliding window method was used to extract patch images and labels from orthomosaic images for training and testing. The performance of the improved method was compared with the baseline model using different loss functions and normalization methods. The distribution of leafroll disease severity was mapped in vineyards using the trained CA-Swin Transformer model. [Results and Discussions] The experimental results showed that the GANformer could generate high-quality virtual canopy images of grapevines with an FID score of 93.20. The images generated by GANformer were visually very similar to real images and could produce images with different levels of leafroll disease severity. The T-SNE visualization showed that the features of real and simulated images were well clustered and separated in two-dimensional space, indicating that GANformer learned meaningful and diverse features, which enriched the image dataset. Compared to CNN-based deep learning models, Transformer-based deep learning models had more advantages in diagnosing leafroll disease infection. Swin Transformer achieved an optimal accuracy of 83.97% on the enhanced dataset, which was higher than other models such as GoogLeNet, MobileNetV2, NasNet Mobile, ResNet18, ResNet50, CVT, and T2TViT. It was found that replacing the cross entropy loss function with the ArcFace loss function improved the classification accuracy by 1.50%, and applying instance normalization instead of layer normalization further improved the accuracy by 0.30%. Moreover, the proposed channel attention mechanism, named CA-Swin Transformer, enhanced the feature representation of the Swin Transformer model, achieved the highest classification accuracy on the test set, reaching 86.65%, which was 6.54% higher than using the Swin Transformer on the original test dataset. By creating a distribution map of leafroll disease severity in vineyards, it was found that there was a certain correlation between leafroll disease severity and grape rows. Areas with a larger number of severe leafroll diseases caused by Cabernet Sauvignon were more prone to have missing or weak plants. [Conclusions] A novel method for diagnosing grapevine leafroll disease severity at a canopy scale using UAV remote sensing technology and deep learning was proposed. This method can generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity using GANformer, and classify them into different classes using CA-Swin Transformer. This method can also map the distribution of leafroll disease severity in vineyards using a sliding window method, and provides a new approach for crop disease monitoring based on UAV remote sensing technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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CSD-YOLOv8s: Dense Sheep Small Target Detection Model Based on UAV Images WENG Zhi, LIU Haixin, ZHENG Zhiqiang Smart Agriculture    2024, 6 (4): 42-52.   DOI: 10.12133/j.smartag.SA202401004 Abstract （1469）   HTML （114）    PDF（pc） （1772KB）（1374）       Knowledge map    Save [Objective] The monitoring of livestock grazing in natural pastures is a key aspect of the transformation and upgrading of large-scale breeding farms. In order to meet the demand for large-scale farms to achieve accurate real-time detection of a large number of sheep, a high-precision and easy-to-deploy small-target detection model: CSD-YOLOv8s was proposed to realize the real-time detection of small-targeted individual sheep under the high-altitude view of the unmanned aerial vehicle (UAV). [Methods] Firstly, a UAV was used to acquire video data of sheep in natural grassland pastures with different backgrounds and lighting conditions, and together with some public datasets downloaded formed the original image data. The sheep detection dataset was generated through data cleaning and labeling. Secondly, in order to solve the difficult problem of sheep detection caused by dense flocks and mutual occlusion, the SPPFCSPC module was constructed with cross-stage local connection based on the you only look once (YOLO)v8 model, which combined the original features with the output features of the fast spatial pyramid pooling network, fully retained the feature information at different stages of the model, and effectively solved the problem of small targets and serious occlusion of the sheep, and improved the detection performance of the model for small sheep targets. In the Neck part of the model, the convolutional block attention module (CBAM) convolutional attention module was introduced to enhance the feature information capture based on both spatial and channel aspects, suppressing the background information spatially and focusing on the sheep target in the channel, enhancing the network's anti-jamming ability from both channel and spatial dimensions, and improving the model's detection performance of multi-scale sheep under complex backgrounds and different illumination conditions. Finally, in order to improve the real-time and deploy ability of the model, the standard convolution of the Neck network was changed to a lightweight convolutional C2f_DS module with a changeable kernel, which was able to adaptively select the corresponding convolutional kernel for feature extraction according to the input features, and solved the problem of input scale change in the process of sheep detection in a more flexible way, and at the same time, the number of parameters of the model was reduced and the speed of the model was improved. [Results and Discussions] The improved CSD-YOLOv8s model exhibited excellent performance in the sheep detection task. Compared with YOLO, Faster R-CNN and other classical network models, the improved CSD-YOLOv8s model had higher detection accuracy and frames per second (FPS) of 87 f/s in the flock detection task with comparable detection speed and model size. Compared with the YOLOv8s model, Precision was improved from 93.0% to 95.2%, mAP was improved from 91.2% to 93.1%, and it had strong robustness to sheep targets with different degree of occlusion and different scales, which effectively solved the serious problems of missed and misdetection of sheep in the grassland pasture UAV-on-ground sheep detection task due to the small sheep targets, large background noise, and high degree of densification. misdetection serious problems. Validated by the PASCAL VOC 2007 open dataset, the CSD-YOLOv8s model proposed in this study improved the detection accuracy of 20 different objects, including transportation vehicles, animals, etc., especially in sheep detection, the detection accuracy was improved by 9.7%. [Conclusions] This study establishes a sheep dataset based on drone images and proposes a model called CSD-YOLOv8s for detecting grazing sheep in natural grasslands. The model addresses the serious issues of missed detections and false alarms in sheep detection under complex backgrounds and lighting conditions, enabling more accurate detection of grazing livestock in drone images. It achieves precise detection of targets with varying degrees of clustering and occlusion and possesses good real-time performance. This model provides an effective detection method for detecting sheep herds from the perspective of drones in natural pastures and offers technical support for large-scale livestock detection in breeding farms, with wide-ranging potential applications. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Technological Revolution, Disruptive Technology and Smart Agriculture HU Ruifa, LIU Wanjiawen Smart Agriculture    2022, 4 (4): 138-143.   DOI: 10.12133/j.smartag.SA202205002 Abstract （1403）   HTML （151）    PDF（pc） （475KB）（1363）       Knowledge map    Save This paper described the concept and basic satisfaction of scientific and technological revolution, defines the endogenous and exogenous agricultural disruptive technology. The revolution of agricultural science and technology refers to the process in which the key disruptive core technology innovation applied to agricultural production drives a series of technological innovations adopted in production. Endogenous disruptive technology in agriculture refers to technology indicators that can fundamentally change the original technology, such as productivity improvement, overturn the economic or social necessity of the adoption of the original technology, and completely replace the original technology. Particularly the paper puts forwards the concept of the transboundary technology and demonstrates its endogenous application and impacts on the development of agricultural industry. The transboundary technology for exogenous application refers to the technology whose original invention and innovation are applied in non-agricultural fields and has nothing to do with agricultural industry. Focusing on smart agriculture and the typical transboundary technology, the paper analyzed the characteristics of the smart agriculture, discussed its impacts on the traditional agricultural production and rural transformation. Smart agriculture technology will be the disruptive core technology to promote a new round of technological and industrial revolution and rural transformation. It will fundamentally change the production mode of traditional agriculture, realize factory production and promote the revolutionary transformation of rural areas. The production and application of smart agricultural technology in China has shown good economic and social benefits and great potential for production and application. However, the application of intelligent agricultural technology based on artificial intelligence technology is still in the exploratory stage. As an agricultural application of transboundary technology, the smart agricultural technology with intelligent sensing technology as the core is not dominated by agricultural scientists like agricultural machinery technology revolution, chemical technology revolution and green revolution technology. At present, the application smart agriculture technology in China is only in its infancy. Hence, policy recommendations of strengthening key disruptive technology development, reforming agricultural higher education system, promoting the agricultural industry development of the transboundary technology, and pushing the application of smart agriculture technology to be implemented in high standard farmland and large-scale farms of agricultural production, etc., were proposed to promote the development of smart agriculture. Reference | Related Articles | Metrics | Comments（0）

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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min Smart Agriculture    2024, 6 (5): 119-127.   DOI: 10.12133/j.smartag.SA202403016 Abstract （423）   HTML （44）    PDF（pc） （1379KB）（1361）       Knowledge map    Save [Objective] The picking of famous and high-quality tea is a crucial link in the tea industry. Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. In this study, based on the actual scenario of the Xiqing Tea Garden in Hunan Province, proposes a novel deep learning algorithm was proposed to solve the precise segmentation challenge of famous and high-quality tea picking points. [Methods] The primary technical innovation resided in the amalgamation of a lightweight network architecture, MobilenetV2, with an attention mechanism known as efficient channel attention network (ECANet), alongside optimization modules including atrous spatial pyramid pooling (ASPP). Initially, MobilenetV2 was employed as the feature extractor, substituting traditional convolution operations with depth wise separable convolutions. This led to a notable reduction in the model's parameter count and expedited the model training process. Subsequently, the innovative fusion of ECANet and ASPP modules constituted the ECA_ASPP module, with the intention of bolstering the model's capacity for fusing multi-scale features, especially pertinent to the intricate recognition of tea shoots. This fusion strategy facilitated the model's capability to capture more nuanced features of delicate shoots, thereby augmenting segmentation accuracy. The specific implementation steps entailed the feeding of image inputs through the improved network, whereupon MobilenetV2 was utilized to extract both shallow and deep features. Deep features were then fused via the ECA_ASPP module for the purpose of multi-scale feature integration, reinforcing the model's resilience to intricate backgrounds and variations in tea shoot morphology. Conversely, shallow features proceeded directly to the decoding stage, undergoing channel reduction processing before being integrated with upsampled deep features. This divide-and-conquer strategy effectively harnessed the benefits of features at differing levels of abstraction and, furthermore, heightened the model's recognition performance through meticulous feature fusion. Ultimately, through a sequence of convolutional operations and upsampling procedures, a prediction map congruent in resolution with the original image was generated, enabling the precise demarcation of tea shoot harvesting points. [Results and Discussions] The experimental outcomes indicated that the enhanced DeepLabV3+ model had achieved an average Intersection over Union (IoU) of 93.71% and an average pixel accuracy of 97.25% on the dataset of tea shoots. Compared to the original model based on Xception, there was a substantial decrease in the parameter count from 54.714 million to a mere 5.818 million, effectively accomplishing a significant lightweight redesign of the model. Further comparisons with other prevalent semantic segmentation networks revealed that the improved model exhibited remarkable advantages concerning pivotal metrics such as the number of parameters, training duration, and average IoU, highlighting its efficacy and precision in the domain of tea shoot recognition. This considerable decreased in parameter numbers not only facilitated a more resource-economical deployment but also led to abbreviated training periods, rendering the model highly suitable for real-time implementations amidst tea garden ecosystems. The elevated mean IoU and pixel accuracy attested to the model's capacity for precise demarcation and identification of tea shoots, even amidst intricate and varied datasets, demonstrating resilience and adaptability in pragmatic contexts. [Conclusions] This study effectively implements an efficient and accurate tea shoot recognition method through targeted model improvements and optimizations, furnishing crucial technical support for the practical application of intelligent tea picking robots. The introduction of lightweight DeepLabV3+ not only substantially enhances recognition speed and segmentation accuracy, but also mitigates hardware requirements, thereby promoting the practical application of intelligent picking technology in the tea industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Collaborative Computing of Food Supply Chain Privacy Data Elements Based on Federated Learning XU Jiping, LI Hui, WANG Haoyu, ZHOU Yan, WANG Zhaoyang, YU Chongchong Smart Agriculture    2023, 5 (4): 79-91.   DOI: 10.12133/j.smartag.SA202309012 Abstract （457）   HTML （30）    PDF（pc） （1719KB）（1359）       Knowledge map    Save [Objective] The flow of private data elements plays a crucial role in the food supply chain, and the safe and efficient operation of the food supply chain can be ensured through the effective management and flow of private data elements. Through collaborative computing among the whole chain of the food supply chain, the production, transportation and storage processes of food can be better monitored and managed, so that possible quality and safety problems can be detected and solved in a timely manner, and the health and rights of consumers can be safeguarded. It can also be applied to the security risk assessment and early warning of the food supply chain. By analyzing big data, potential risk factors and abnormalities can be identified, and timely measures can be taken for early warning and intervention to reduce the possibility of quality and safety risks. This study combined the industrial Internet identification and resolution system with the federated learning algorithm, which can realize collaborative learning among multiple enterprises, and each enterprise can carry out collaborative training of the model without sharing the original data, which protects the privacy and security of the data while realizing the flow of the data, and it can also make use of the data resources distributed in different segments, which can realize more comprehensive and accurate collaborative calculations, and improve the safety and credibility of the industrial Internet system's security and credibility. [Methods] To address the problem of not being able to share and participate in collaborative computation among different subjects in the grain supply chain due to the privacy of data elements, this study first analyzed and summarized the characteristics of data elements in the whole link of grain supply chain, and proposed a grain supply chain data flow and collaborative computation architecture based on the combination of the industrial Internet mark resolution technology and the idea of federated learning, which was constructed in a layered and graded model to provide a good infrastructure for the decentralized between the participants. The data identification code for the flow of food supplied chain data elements and the task identification code for collaborative calculation of food supply chain, as well as the corresponding parameter data model, information data model and evaluation data model, were designed to support the interoperability of federated learning data. A single-link horizontal federation learning model with isomorphic data characteristics of different subjects and a cross-link vertical federation learning model with heterogeneous data characteristics were constructed, and the model parameters were quickly adjusted and calculated based on logistic regression algorithm, neural network algorithm and other algorithms, and the food supply chain security risk assessment scenario was taken as the object of the research, and the research was based on the open source FATE (Federated AI Technology) federation learning model. Enabler (Federated AI Technology) federated learning platform for testing and validation, and visualization of the results to provide effective support for the security management of the grain supply chain. [Results and Discussion] Compared with the traditional single-subject assessment calculation method, the accuracy of single-session isomorphic horizontal federation learning model assessment across subjects was improved by 6.7%, and the accuracy of heterogeneous vertical federation learning model assessment across sessions and subjects was improved by 8.3%. This result showed that the single-session isomorphic horizontal federated learning model assessment across subjects could make full use of the data information of each subject by merging and training the data of different subjects in the same session, thus improving the accuracy of security risk assessment. The heterogeneous vertical federated learning model assessment of cross-session and cross-subject further promotes the application scope of collaborative computing by jointly training data from different sessions and subjects, which made the results of safety risk assessment more comprehensive and accurate. The advantage of combining federated learning and logo resolution technology was that it could conduct model training without sharing the original data, which protected data privacy and security. At the same time, it could also realize the effective use of data resources and collaborative computation, improving the efficiency and accuracy of the assessment process. [Conclusions] The feasibility and effectiveness of this study in practical applications in the grain industry were confirmed by the test validation of the open-source FATE federated learning platform. This provides reliable technical support for the digital transformation of the grain industry and the security management of the grain supply chain, and helps to improve the intelligence level and competitiveness of the whole grain industry. Therefore, this study can provide a strong technical guarantee for realizing the safe, efficient and sustainable development of the grain supply chain. 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Traceability Model of Plantation Agricultural Products Based on Blockchain and InterPlanetary File System CHEN Dandan, ZHANG Lijie, JIANG Shuangfeng, ZHANG En, ZHANG Jie, ZHAO Qing, ZHENG Guoqing, LI Guoqiang Smart Agriculture    2023, 5 (4): 68-78.   DOI: 10.12133/j.smartag.SA202307004 Abstract （494）   HTML （37）    PDF（pc） （2011KB）（1345）       Knowledge map    Save [Objective] The InterPlanetary File System (IPFS) is a peer-to-peer distributed file system, aiming to establish a global, open, and decentralized network for storage and sharing. Combining the IPFS and blockchain technology could alleviate the pressure on blockchain storage. The distinct features of the supply chain for agricultural products in the plantation industry, including extended production cycles, multiple, heterogeneous data sources, and relatively fragmented production, which can readily result in information gaps and opacity throughout the supply chain; in the traceability process of agricultural products, there are issues with sensitive data being prone to leakage and a lack of security, and the supply chain of plantation agricultural products is long, and the traceability data is often stored in multiple blocks, which requires frequent block tracing operations during tracing, resulting in low efficiency. Consequently, the aim of this study is to fully encapsulate the decentralized nature of blockchain, safeguard the privacy of sensitive data, and alleviate the storage strain of blockchain. [Method] A traceability model for plantation-based agricultural products was developed, leveraging the hyperledger fabric consortium chain and the IPFS. Based on data type, traceability data was categorized into structured and unstructured data. Given that blockchain ledgers were not optimized for direct storage of unstructured data, such as images and videos, to alleviate the storage strain on the blockchain, unstructured data was persisted in the IPFS, while structured data remains within the blockchain ledger. Based on data privacy categories, traceability data was categorized into public data and sensitive data. Public data was stored in the public ledger of hyperledger fabric, while sensitive data was stored in the private data collection of hyperledger fabric. This method allowed for efficient data access while maintaining data security, enhancing the efficiency of traceability. Hyperledger Fabric was the foundational platform for the development of the prototype system. The front-end website was based on the TCP/IP protocol stack. The website visualization was implemented through the React framework. Smart contracts were crafted using the Java programming language. The performance of the application layer interface was tested using the testing tool Postman. [Conclusions and Discussions] The blockchain-based plantation agricultural product traceability system was structured into a five-tiered architecture, starting from the top: the application layer, gateway layer, contract layer, consensus layer, and data storage layer. The primary service providers at the application layer were the enterprises and consumers involved in each stage of the traceability process. The gateway layer served as the middleware between users and the blockchain, primarily providing interface support for the front-end interface of the application layer. The contract layer mainly included smart contracts for planting, processing, warehousing, transportation, and sales. The consensus layer used the EtcdRaft consensus algorithm. The data storage layer was divided into the on-chain storage layer of the blockchain ledger and the off-chain storage layer of the IPFS cluster. In terms of data types, each piece of traceability data was categorized into structured data items and unstructured data items. Unstructured data was stored in the Interstellar File System cluster, and the returned content identifiers were integrated with the structured data items into the blockchain nodes within the traceability system. In the realm of data privacy, smart contracts were employed to segregate public and sensitive data, with public data directly integrating onto the blockchain, and sensitive data, adhering to predefined sharing policies, being stored in a private dataset designated by hyperledger fabric. In terms of user queries, consumers could retrieve product traceability information via a traceability system overseen by a reputable authority. The developed model website consisted of three parts: a login section, an agricultural product circulation information management and user data management section for enterprises in various links, and a traceability data query section for consumers. When using synchronous and asynchronous Application Program Interfaces, the average data on-chain latency was 2 138.9 and 37.6 ms, respectively, and the average data query latency was 12.3 ms. Blockchain, as the foundational data storage technology, enhances the credibility and transaction efficiency in agricultural product traceability. [Conclusions] This study designed and implemented a plantation agricultural product traceability model leveraging blockchain technology's private dataset and the IPFS cluster. This model ensured secure sharing and storage of traceability data, particularly sensitive data, across all stages. Compared to traditional centralized traceability models, it enhanced the reliability of the traceability data. Based on the evaluation through experimental systems, the traceability model proposed in this study effectively safeguarded the privacy of sensitive data in enterprises. Additionally, it offered high efficiency in data linking and querying. Applicable to the real-world traceability environment of plantation agricultural products, it showed potential for widespread application and promotion, offering fresh insights for designing blockchain traceability models in this sector. The model is still in its experimental phase and lacks applications across various types of crops in the farming industry. The subsequent step is to apply the model in real-world scenarios, continually enhance its efficiency, refine the model, advance the practical application of blockchain technology, and lay the foundation for agricultural modernization. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Identification and Severity Classification of Typical Maize Foliar Diseases Based on Hyperspectral Data SHEN Yanyan, ZHAO Yutao, CHEN Gengshen, LYU Zhengang, ZHAO Feng, YANG Wanneng, MENG Ran Smart Agriculture    2024, 6 (2): 28-39.   DOI: 10.12133/j.smartag.SA202310016 Abstract （1357）   HTML （83）    PDF（pc） （1519KB）（1345）       Knowledge map    Save [Objective] In recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields. [Methods] A method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F1 were used to evaluate the model accuracy in this study. Results and Discussion The research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared (1 300-1 800 nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F1 of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F1 compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F1 of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F1 of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F1=0.70) and SCR severity classification model (OA=71.39%, Macro F1=0.69) were lower than that of NCLB. [Conclusions] The aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for large-scale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Using a Portable Visible-near Infrared Spectrometer and Machine Learning to Distinguish and Quantify Mold Contamination in Wheat JIA Wenshen, LYU Haolin, ZHANG Shang, QIN Yingdong, ZHOU Wei Smart Agriculture    2024, 6 (1): 89-100.   DOI: 10.12133/j.smartag.SA202311032 Abstract （432）   HTML （24）    PDF（pc） （2050KB）（1344）       Knowledge map    Save Objective Traditional methods for detecting mold are time-consuming, labor-intensive, and vulnerable to environmental influences, highlighting the need for a swift, precise, and dependable detection approach. Researchers have utilized visible-near infrared (NIR) spectroscopy for the non-destructive, rapid assessment of wheat moisture content, crude protein content, concealed pests, starch content, dry matter, weight, hardness, origin, and other attributes. However, most of these studies rely on research-grade Visible-NIR spectrometers typically found in laboratories. While these spectrometers offer superior detection accuracy and stability, their bulky size, lack of portability, and high cost hinder their widespread use and adoption across various agricultural product distribution channels. Methods A low-resolution Visible-NIR spectrometer (VNIAPD, with a resolution of 1.6 nm) was utilized to gather wheat data. The aim was to enhance the accuracy of moldy wheat detection by identifying suitable spectral data preprocessing methods using corresponding algorithms. A high-resolution Visible-NIR spectrometer (SINO2040, with a resolution of 0.19 nm) served as a control to validate the instrument and method's effectiveness. The Zhoumai (No. 22) wheat variety was adopted, with a total of 100 samples prepared. The spectra of fresh wheat were scanned and then placed in a constant temperature chamber at 35 °C to replicate the appropriate conditions for mold growth, thereby accelerating the reproduction of naturally occurring mold in the wheat. The degree of mold was categorized based on the cultivation time in the constant temperature chamber, with wheat classified as mildly, moderately, or severely moldy after 3, 6, and 9 days of cultivation, respectively. A total of 400 wheat spectral data points were collected, including 100 samples each of fresh wheat, wheat cultured for 3 days, wheat cultured for 6 days, and wheat cultured for 9 days. Preprocessing methods such as standard deviation normalization (SDN), standard normal variation (SNV), mean centrality (MC), first-order derivatives (1ST), Savitzky-Golay smoothing (SG), and multiple scattering correction (MSC) were applied to the spectral data. Outliers were identified and eliminated using the local outlier factor (LOF) method. Following this, the sequential projection algorithm (SPA) and Least absolute shrinkage and selection operator (LASSO) were used to extract characteristic wavelengths from the preprocessed spectra. Subsequently, six algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), Naïve-Bayes, back propagation neural networks (BPNN), and deep neural networks (DNN), were employed to model and analyze the feature wavelength spectra, differentiating moldy wheat and classifying the degree of mold. Evaluation criteria encompassed accuracy, modeling time, and model size to aid in selecting the most suitable model for specific application scenarios. Results and discussions Regarding accuracy, even when utilizing the computationally slower and more memory-demanding neural network models BPNN and DNN, both the VNIAPD and SINO2040 achieved a perfect 100% accuracy in the binary classification task of distinguishing between fresh and moldy wheat. They also maintained a faultless 100% accuracy in the ternary classification task that differentiates three varying levels of mold growth. Adopting faster and more memory-efficient shallow models such as KNN, SVM, RF, and Naïve-Bayes, the VNIAPD yielded a top test set accuracy of 97.72% when combined with RF for binary classification. Conversely, SINO2040 achieved 100% accuracy using Naïve-Bayes. In the ternary classification scenario, the VNIAPD hit the mark at 100% accuracy with both KNN and RF, while SINO2040 demonstrated 97.72% accuracy with KNN and SVM. Regarding modeling speed, the shallow machine learning algorithms, including KNN, SVM, RF, and Naïve-Bayes, exhibited quicker training times, with Naïve-Bayes being the swiftest at just 3 ms. In contrast, the neural network algorithms BPNN and DNN required more time for training, taking 3 293 and 18 614 ms, respectively. Regarding memory footprint, BPNN had the largest model size, occupying 4 028 kb, whereas SVM was the most memory-efficient, with a size of only 4 kb. Overall, the VNIAPD matched the SINO2040 in detection accuracy despite having lower optical parameters: A slightly lesser optical resolution of 1.6 nm compared to the SINO2040's 0.19 nm—and a lower cost, highlighting its efficiency and cost-effectiveness in the given context. Conclusions In this study, by comparing different preprocessing methods for spectral data, the optimal data optimization choices for corresponding algorithms were identified. As a result, the low-resolution spectrometer VNIAPD was able to achieve performance on par with the high-resolution spectrometer SINO2040 in detecting moldy wheat, providing a new option for low-cost, non-destructive detection of wheat mold and the degree of moldiness based on Visible-NIR spectroscopy. Table and Figures | Reference | Related Articles | Metrics | Comments（0）