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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 （856）   HTML （275）    PDF（pc） （3579KB）（1114）       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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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 （366）   HTML （94）    PDF（pc） （2822KB）（683）       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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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 （399）   HTML （112）    PDF（pc） （1576KB）（664）       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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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 （226）   HTML （42）    PDF（pc） （1659KB）（642）       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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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 （476）   HTML （143）    PDF（pc） （2277KB）（607）       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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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 （206）   HTML （44）    PDF（pc） （1858KB）（572）       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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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 （261）   HTML （50）    PDF（pc） （1686KB）（542）       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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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 （492）   HTML （131）    PDF（pc） （2531KB）（519）       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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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 （423）   HTML （134）    PDF（pc） （837KB）（491）       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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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 （157）   HTML （21）    PDF（pc） （1531KB）（427）       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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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 （236）   HTML （53）    PDF（pc） （4042KB）（423）       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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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 （281）   HTML （51）    PDF（pc） （1400KB）（403）       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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Rapid Recognition and Picking Points Automatic Positioning Method for Table Grape in Natural Environment ZHU Yanjun, DU Wensheng, WANG Chunying, LIU Ping, LI Xiang Smart Agriculture    2023, 5 (2): 23-34.   DOI: 10.12133/j.smartag.SA202304001 Abstract （298）   HTML （53）    PDF（pc） （2122KB）（355）       Knowledge map    Save [Objective] Rapid recognition and automatic positioning of table grapes in the natural environment is the prerequisite for the automatic picking of table grapes by the picking robot. [Methods] An rapid recognition and automatic picking points positioning method based on improved K-means clustering algorithm and contour analysis was proposed. First, euclidean distance was replaced by a weighted gray threshold as the judgment basis of K-means similarity. Then the images of table grapes were rasterized according to the K value, and the initial clustering center was obtained. Next, the average gray value of each cluster and the percentage of pixel points of each cluster in the total pixel points were calculated. And the weighted gray threshold was obtained by the average gray value and percentage of adjacent clusters. Then, the clustering was considered as have ended until the weighted gray threshold remained unchanged. Therefore, the cluster image of table grape was obtained. The improved clustering algorithm not only saved the clustering time, but also ensured that the K value could change adaptively. Moreover, the adaptive Otsu algorithm was used to extract grape cluster information, so that the initial binary image of the table grape was obtained. In order to reduce the interference of redundant noise on recognition accuracy, the morphological algorithms (open operation, close operation, images filling and the maximum connected domain) were used to remove noise, so the accurate binary image of table grapes was obtained. And then, the contours of table grapes were obtained by the Sobel operator. Furthermore, table grape clusters grew perpendicular to the ground due to gravity in the natural environment. Therefore, the extreme point and center of gravity point of the grape cluster were obtained based on contour analysis. In addition, the linear bundle where the extreme point and the center of gravity point located was taken as the carrier, and the similarity of pixel points on both sides of the linear bundle were taken as the judgment basis. The line corresponding to the lowest similarity value was taken as the grape stem, so the stem axis of the grape was located. Moreover, according to the agronomic picking requirements of table grapes, and combined with contour analysis, the region of interest (ROI) in picking points could be obtained. Among them, the intersection of the grapes stem and the contour was regarded as the middle point of the bottom edge of the ROI. And the 0.8 times distance between the left and right extreme points was regarded as the length of the ROI, the 0.25 times distance between the gravity point and the intersection of the grape stem and the contour was regarded as the height of the ROI. After that, the central point of the ROI was captured. Then, the nearest point between the center point of the ROI and the grape stem was determined, and this point on the grape stem was taken as the picking point of the table grapes. Finally, 917 grape images (including Summer Black, Moldova, and Youyong) taken by the rear camera of MI8 mobile phone at Jinniu Mountain Base of Shandong Fruit and Vegetable Research Institute were verified experimentally. Results and Discussions] The results showed that the success rate was 90.51% when the error between the table grape picking points and the optimal points were less than 12 pixels, and the average positioning time was 0.87 s. The method realized the fast and accurate localization of table grape picking points. On top of that, according to the two cultivation modes (hedgerow planting and trellis planting) of table grapes, a simulation test platform based on the Dense mechanical arm and the single-chip computer was set up in the study. 50 simulation tests were carried out for the four conditions respectively, among which the success rate of localization for purple grape picking point of hedgerow planting was 86.00%, and the average localization time was 0.89 s; the success rate of localization for purple grape identification and localization of trellis planting was 92.00%, and the average localization time was 0.67 s; the success rate of localization for green grape picking point of hedgerow planting was 78.00%, and the average localization time was 0.72 s; and the success rate of localization for green grape identification and localization of trellis planting was 80.00%, and the average localization time was 0.71 s. [Conclusions] The experimental results showed that the method proposed in the study can meet the requirements of table grape picking, and can provide technical supports for the development of grape picking robot. 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 （568）   HTML （146）    PDF（pc） （2498KB）（349）       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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Yield Prediction Models in Guangxi Sugarcane Planting Regions Based on Machine Learning Methods SHI Jiefeng, HUANG Wei, FAN Xieyang, LI Xiuhua, LU Yangxu, JIANG Zhuhui, WANG Zeping, LUO Wei, ZHANG Muqing Smart Agriculture    2023, 5 (2): 82-92.   DOI: 10.12133/j.smartag.SA202304004 Abstract （235）   HTML （42）    PDF（pc） （1175KB）（318）       Knowledge map    Save [Objective] Accurate prediction of changes in sugarcane yield in Guangxi can provide important reference for the formulation of relevant policies by the government and provide decision-making basis for farmers to guide sugarcane planting, thereby improving sugarcane yield and quality and promoting the development of the sugarcane industry. This research was conducted to provide scientific data support for sugar factories and related management departments, explore the relationship between sugarcane yield and meteorological factors in the main sugarcane producing areas of Guangxi Zhuang Autonomous Region. [Methods] The study area included five sugarcane planting regions which laid in five different counties in Guangxi, China. The average yields per hectare of each planting regions were provided by Guangxi Sugar Industry Group which controls the sugar refineries of each planting region. The daily meteorological data including 14 meteorological factors from 2002 to 2019 were acquired from National Data Center for Meteorological Sciences to analyze their influences placed on sugarcane yield. Since meteorological factors could pose different influences on sugarcane growth during different time spans, a new kind of factor which includes meteorological factors and time spans was defined, such as the average precipitation in August, the average temperature from February to April, etc. And then the inter-correlation of all the meteorological factors of different time spans and their correlations with yields were analyzed to screen out the key meteorological factors of sensitive time spans. After that, four algorithms of BP neural network (BPNN), support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) were employed to establish sugarcane apparent yield prediction models for each planting region. Their corresponding reference models based on the annual meteorological factors were also built. Additionally, the meteorological yields of every planting region were extracted by HP filtering, and a general meteorological yield prediction model was built based on the data of all the five planting regions by using RF, SVM BPNN, and LSTM, respectively. [Results and Discussions] The correlation analysis showed that different planting regions have different sensitive meteorological factors and key time spans. The highly representative meteorological factors mainly included sunshine hours, precipitation, and atmospheric pressure. According to the results of correlation analysis, in Region 1, the highest negative correlation coefficient with yield was observed at the sunshine hours during October and November, while the highest positive correlation coefficient was found at the minimum relative humidity in November. In Region 2, the maximum positive correlation coefficient with yield was observed at the average vapor pressure during February and March, whereas the maximum negative correlation coefficient was associated with the precipitation in August and September. In Region 3, the maximum positive correlation coefficient with yield was found at the 20‒20 precipitation during August and September, while the maximum negative correlation coefficient was related to sunshine hours in the same period. In Region 4, the maximum positive correlation coefficient with yield was observed at the 20‒20 precipitation from March to December, whereas the maximum negative correlation coefficient was associated with the highest atmospheric pressure from August to December. In Region 5, the maximum positive correlation coefficient with yield was found at the average vapor pressure from June and to August, whereas the maximum negative correlation coefficient as related to the lowest atmospheric pressure in February and March. For each specific planting region, the accuracy of apparent yield prediction model based on sensitive meteorological factors during key time spans was obviously better than that based on the annual average meteorological values. The LSTM model performed significantly better than the widely used classic BPNN, SVM, and RF models for both kinds of meteorological factors (under sensitive time spans or annually). The overall root mean square error (RMSE) and mean absolute percentage error (MAPE) of the LSTM model under key time spans were 10.34 t/ha and 6.85%, respectively, with a coefficient of determination Rv2 of 0.8489 between the predicted values and true values. For the general prediction models of the meteorological yield to multiple the sugarcane planting regions, the RF, SVM, and BPNN models achieved good results, and the best prediction performance went to BPNN model, with an RMSE of 0.98 t/ha, MAPE of 9.59%, and Rv2 of 0.965. The RMSE and MAPE of the LSTM model were 0.25 t/ha and 39.99%, respectively, and the Rv2 was 0.77. [Conclusions] Sensitive meteorological factors under key time spans were found to be more significantly correlated with the yields than the annual average meteorological factors. LSTM model shows better performances on apparent yield prediction for specific planting region than the classic BPNN, SVM, and RF models, but BPNN model showed better results than other models in predicting meteorological yield over multiple sugarcane planting regions. Table and Figures | 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 （221）   HTML （53）    PDF（pc） （3593KB）（294）       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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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 （317）   HTML （72）    PDF（pc） （3044KB）（275）       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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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 （204）   HTML （45）    PDF（pc） （1664KB）（244）       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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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 （249）   HTML （47）    PDF（pc） （1475KB）（223）       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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Identification Method of Wheat Field Lodging Area Based on Deep Learning Semantic Segmentation and Transfer Learning ZHANG Gan, YAN Haifeng, HU Gensheng, ZHANG Dongyan, CHENG Tao, PAN Zhenggao, XU Haifeng, SHEN Shuhao, ZHU Keyu Smart Agriculture    2023, 5 (3): 75-85.   DOI: 10.12133/j.smartag.SA202309013 Abstract （151）   HTML （34）    PDF（pc） （2219KB）（212）       Knowledge map    Save [Objective] Lodging constitutes a severe crop-related catastrophe, resulting in a reduction in photosynthesis intensity, diminished nutrient absorption efficiency, diminished crop yield, and compromised crop quality. The utilization of unmanned aerial vehicles (UAV) to acquire agricultural remote sensing imagery, despite providing high-resolution details and clear indications of crop lodging, encounters limitations related to the size of the study area and the duration of the specific growth stages of the plants. This limitation hinders the acquisition of an adequate quantity of low-altitude remote sensing images of wheat fields, thereby detrimentally affecting the performance of the monitoring model. The aim of this study is to explore a method for precise segmentation of lodging areas in limited crop growth periods and research areas. [Methods] Compared to the images captured at lower flight altitudes, the images taken by UAVs at higher altitudes cover a larger area. Consequently, for the same area, the number of images taken by UAVs at higher altitudes is fewer than those taken at lower altitudes. However, the training of deep learning models requires huge amount supply of images. To make up the issue of insufficient quantity of high-altitude UAV-acquired images for the training of the lodging area monitoring model, a transfer learning strategy was proposed. In order to verify the effectiveness of the transfer learning strategy, based on the Swin-Transformer framework, the control model, hybrid training model and transfer learning training model were obtained by training UAV images in 4 years (2019, 2020, 2021, 2023)and 3 study areas(Shucheng, Guohe, Baihe) under 2 flight altitudes (40 and 80 m). To test the model's performance, a comparative experimental approach was adopted to assess the accuracy of the three models for segmenting 80 m altitude images. The assessment relied on five metrics: intersection of union (IoU), accuracy, precision, recall, and F1-score. [Results and Discussions] The transfer learning model shows the highest accuracy in lodging area detection. Specifically, the mean IoU, accuracy, precision, recall, and F1-score achieved 85.37%, 94.98%, 91.30%, 92.52% and 91.84%, respectively. Notably, the accuracy of lodging area detection for images acquired at a 40 m altitude surpassed that of images captured at an 80 m altitude when employing a training dataset composed solely of images obtained at the 40 m altitude. However, when adopting mixed training and transfer learning strategies and augmenting the training dataset with images acquired at an 80 m altitude, the accuracy of lodging area detection for 80 m altitude images improved, inspite of the expense of reduced accuracy for 40 m altitude images. The performance of the mixed training model and the transfer learning model in lodging area detection for both 40 and 80 m altitude images exhibited close correspondence. In a cross-study area comparison of the mean values of model evaluation indices, lodging area detection accuracy was slightly higher for images obtained in Baihu area compared to Shucheng area, while accuracy for images acquired in Shucheng surpassed that of Guohe. These variations could be attributed to the diverse wheat varieties cultivated in Guohe area through drill seeding. The high planting density of wheat in Guohe resulted in substantial lodging areas, accounting for 64.99% during the late mature period. The prevalence of semi-lodging wheat further exacerbated the issue, potentially leading to misidentification of non-lodging areas. Consequently, this led to a reduction in the recall rate (mean recall for Guohe images was 89.77%, which was 4.88% and 3.57% lower than that for Baihu and Shucheng, respectively) and IoU (mean IoU for Guohe images was 80.38%, which was 8.80% and 3.94% lower than that for Baihu and Shucheng, respectively). Additionally, the accuracy, precision, and F1-score for Guohe were also lower compared to Baihu and Shucheng. [Conclusions] This study inspected the efficacy of a strategy aimed at reducing the challenges associated with the insufficient number of high-altitude images for semantic segmentation model training. By pre-training the semantic segmentation model with low-altitude images and subsequently employing high-altitude images for transfer learning, improvements of 1.08% to 3.19% were achieved in mean IoU, accuracy, precision, recall, and F1-score, alongside a notable mean weighted frame rate enhancement of 555.23 fps/m2. The approach proposed in this study holds promise for improving lodging monitoring accuracy and the speed of image segmentation. In practical applications, it is feasible to leverage a substantial quantity of 40 m altitude UAV images collected from diverse study areas including various wheat varieties for pre-training purposes. Subsequently, a limited set of 80 m altitude images acquired in specific study areas can be employed for transfer learning, facilitating the development of a targeted lodging detection model. Future research will explore the utilization of UAV images captured at even higher flight altitudes for further enhancing lodging area detection efficiency. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Multi-Focal Green Plant Image Fusion Method Based on Stationary Wavelet Transform and Parameter-Adaptation Dual Channel Pulse-Coupled Neural Network LI Jiahao, QU Hongjun, GAO Mingzhe, TONG Dezhi, GUO Ya Smart Agriculture    2023, 5 (3): 121-131.   DOI: 10.12133/j.smartag.SA202308005 Abstract （107）   HTML （17）    PDF（pc） （1435KB）（205）       Knowledge map    Save [Objective] To construct the 3D point cloud model of green plants a large number of clear images are needed. Due to the limitation of the depth of field of the lens, part of the image would be out of focus when the green plant image with a large depth of field is collected, resulting in problems such as edge blurring and texture detail loss, which greatly affects the accuracy of the 3D point cloud model. However, the existing processing algorithms are difficult to take into account both processing quality and processing speed, and the actual effect is not ideal. The purpose of this research is to improve the quality of the fused image while taking into account the processing speed. [Methods] A plant image fusion method based on non-subsampled shearlet transform (NSST) based parameter-adaptive dual channel pulse-coupled neural network (PADC-PCNN) and stationary wavelet transform (SWT) was proposed. Firstly, the RGB image of the plant was separated into three color channels, and the G channel with many features such as texture details was decomposed by NSST in four decomposition layers and 16 directions, which was divided into one group of low frequency subbands and 64 groups of high frequency subbands. The low frequency subband used the gradient energy fusion rule, and the high frequency subband used the PADC-PCNN fusion rule. In addition, the weighting of the eight-neighborhood modified Laplacian operator was used as the link strength of the high-frequency fusion part, which enhanced the fusion effect of the detailed features. At the same time, for the R and B channels with more contour information and background information, a SWT with fast speed and translation invariance was used to suppress the pseudo-Gibbs effect. Through the high-precision and high-stability multi-focal length plant image acquisition system, 480 images of 8 experimental groups were collected. The 8 groups of data were divided into an indoor light group, natural light group, strong light group, distant view group, close view group, overlooking group, red group, and yellow group. Meanwhile, to study the application range of the algorithm, the focus length of the collected clear plant image was used as the reference (18 mm), and the image acquisition was adjusted four times before and after the step of 1.5 mm, forming the multi-focus experimental group. Subjective evaluation and objective evaluation were carried out for each experimental group to verify the performance of the algorithm. Subjective evaluation was analyzed through human eye observation, detail comparison, and other forms, mainly based on the human visual effect. The image fusion effect of the algorithm was evaluated using four commonly used objective indicators, including average gradient (AG), spatial frequency (SF), entropy (EN), and standard deviation (SD). [Results and Discussions] The proposed PADC-PCNN-SWT algorithm and other five algorithms of common fast guided filtering algorithm (FGF), random walk algorithm (RW), non-subsampled shearlet transform based PCNN (NSST-PCNN) algorithm, SWT algorithm and non-subsampled shearlet transform based parameter-adaptive dual-channel pulse-coupled neural network (NSST-PADC) and were compared. In the objective evaluation data except for the red group and the yellow group, each index of the PADC-PCNN-SWT algorithm was second only to the NSST-PADC algorithm, but the processing speed was 200.0% higher than that of the NSST-PADC algorithm on average. At the same time, compared with the FDF, RW, NSST-PCNN, and SWT algorithms, the PADC-PCN -SWT algorithm improved the clarity index by 5.6%, 8.1%, 6.1%, and 17.6%, respectively, and improved the spatial frequency index by 2.9%, 4.8%, 7.1%, and 15.9%, respectively. However, the difference between the two indicators of information entropy and standard deviation was less than 1%, and the influence was ignored. In the yellow group and the red group, the fusion quality of the non-green part of the algorithm based on PADC-PCNN-SWT was seriously degraded. Compared with other algorithms, the sharpness index of the algorithm based on PADC-PCNN-SWT decreased by an average of 1.1%, and the spatial frequency decreased by an average of 5.1%. However, the indicators of the green part of the fused image were basically consistent with the previous several groups of experiments, and the fusion effect was good. Therefore, the algorithm based on PADC-PCNN-SWT only had a good fusion effect on green plants. Finally, by comparing the quality of four groups of fused images with different focal length ranges, the results showed that the algorithm based on PADC-PCNN-SWT had a better contour and color restoration effect for out-of-focus images in the range of 15-21 mm, and the focusing range based on PADC-PCNN-SWT was about 6 mm. [Conclusions] The multi-focal length image fusion algorithm based on PADC-PCNN-SWT achieved better detail fusion performance and higher image fusion efficiency while ensuring fusion quality, providing high-quality data, and saving a lot of time for building 3D point cloud model of green plants. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （185）   HTML （30）    PDF（pc） （2022KB）（198）       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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Spectroscopic Detection of Rice Leaf Blast Infection at Different Leaf Positions at The Early Stages With Solar-Induced Chlorophyll Fluorescence CHENG Yuxin, XUE Bowen, KONG Yuanyuan, YAO Dongliang, TIAN Long, WANG Xue, YAO Xia, ZHU Yan, CAO Weixing, CHENG Tao Smart Agriculture    2023, 5 (3): 35-48.   DOI: 10.12133/j.smartag.SA202309008 Abstract （221）   HTML （34）    PDF（pc） （5433KB）（197）       Knowledge map    Save [Objective] Rice blast is considered as the most destructive disease that threatens global rice production and causes severe economic losses worldwide. The detection of rice blast in an early manner plays an important role in resistance breeding and plant protection. At present, most studies on rice blast detection have been devoted to its symptomatic stage, while none of previous studies have used solar-induced chlorophyll fluorescence (SIF) to monitor rice leaf blast (RLB) at early stages. This research was conducted to investigate the early identification of RLB infected leaves based on solar-induced chlorophyll fluorescence at different leaf positions. [Methods] Greenhouse experiments and field trials were conducted separately in Nanjing and Nantong in July and August, 2021, in order to record SIF data of the top 1th to 4th leaves of rice plants at jointing and heading stages with an Analytical Spectral Devices (ASD) spectrometer coupled with a FluoWat leaf clip and a halogen lamp. At the same time, the disease severity levels of the measured samples were manually collected according to the GB/T 15790-2009 standard. After the continuous wavelet transform (CWT) of SIF spectra, separability assessment and feature selection were applied to SIF spectra. Wavelet features sensitive to RLB were extracted, and the sensitive features and their identification accuracy of infected leaves for different leaf positions were compared. Finally, RLB identification models were constructed based on linear discriminant analysis (LDA). [Results and Discussion] The results showed that the upward and downward SIF in the far-red region of infected leaves at each leaf position were significantly higher than those of healthy leaves. This may be due to the infection of the fungal pathogen Magnaporthe oryzae, which may have destroyed the chloroplast structure, and ultimately inhibited the primary reaction of photosynthesis. In addition, both the upward and downward SIF in the red region and the far-red region increased with the decrease of leaf position. The sensitive wavelet features varied by leaf position, while most of them were distributed in the steep slope of the SIF spectrum and wavelet scales 3, 4 and 5. The sensitive features of the top 1th leaf were mainly located at 665-680 nm, 755-790 nm and 815-830 nm. For the top 2th leaf, the sensitive features were mainly found at 665-680 nm and 815-830 nm. For the top 3th one, most of the sensitive features lay at 690 nm, 755-790 nm and 815-830 nm, and the sensitive bands around 690 nm were observed. The sensitive features of the top 4th leaf were primarily located at 665-680 nm, 725 nm and 815-830 nm, and the sensitive bands around 725 nm were observed. The wavelet features of the common sensitive region (665-680 nm), not only had physiological significance, but also coincided with the chlorophyll absorption peak that allowed for reasonable spectral interpretation. There were differences in the accuracy of RLB identification models at different leaf positions. Based on the upward and downward SIF, the overall accuracies of the top 1th leaf were separately 70% and 71%, which was higher than other leaf positions. As a result, the top 1th leaf was an ideal indicator leaf to diagnose RLB in the field. The classification accuracy of SIF wavelet features were higher than the original SIF bands. Based on CWT and feature selection, the overall accuracy of the upward and downward optimal features of the top 1th to 4th leaves reached 70.13%、63.70%、64.63%、64.53% and 70.90%、63.12%、62.00%、64.02%, respectively. All of them were higher than the canopy monitoring feature F760, whose overall accuracy was 69.79%, 61.31%, 54.41%, 61.33% and 69.99%, 58.79%, 54.62%, 60.92%, respectively. This may be caused by the differences in physiological states of the top four leaves. In addition to RLB infection, the SIF data of some top 3th and top 4th leaves may also be affected by leaf senescence, while the SIF data of top 1th leaf, the latest unfolding leaf of rice plants was less affected by other physical and chemical parameters. This may explain why the top 1th leaf responded to RLB earlier than other leaves. The results also showed that the common sensitive features of the four leaf positions were also concentrated on the steep slope of the SIF spectrum, with better classification performance around 675 and 815 nm. The classification accuracy of the optimal common features, ↑WF832,3 and ↓WF809,3, reached 69.45%, 62.19%, 60.35%, 63.00% and 69.98%, 62.78%, 60.51%, 61.30% for the top 1th to top 4th leaf positions, respectively. The optimal common features, ↑WF832,3 and ↓WF809,3, were both located in wavelet scale 3 and 800-840nm, which may be related to the destruction of the cell structure in response to Magnaporthe oryzae infection. [Conclusions] In this study, the SIF spectral response to RLB was revealed, and the identification models of the top 1th leaf were found to be most precise among the top four leaves. In addition, the common wavelet features sensitive to RLB, ↑WF832,3 and ↓WF809,3, were extracted with the identification accuracy of 70%. The results proved the potential of CWT and SIF for RLB detection, which can provide important reference and technical support for the early, rapid and non-destructive diagnosis of RLB in the field. 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 （125）   HTML （24）    PDF（pc） （1991KB）（195）       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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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 （154）   HTML （29）    PDF（pc） （2442KB）（175）       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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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 （155）   HTML （37）    PDF（pc） （1885KB）（159）       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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Traversal Path Planning for Farmland in Hilly Areas Based on Floyd and Improved Genetic Algorithm ZHOU Longgang, LIU Ting, LU Jinzhu Smart Agriculture    2023, 5 (4): 45-57.   DOI: 10.12133/j.smartag.SA202308004 Abstract （153）   HTML （22）    PDF（pc） （2023KB）（156）       Knowledge map    Save [Objective] To addresses the problem of traversing multiple fields for agricultural robots in hilly terrain, a traversal path planning method is proposed by combining the Floyd algorithm with an improved genetic algorithm. The method provides a solution that can reduce the cost of agricultural robot operation and optimize the order of field traversal in order to improve the efficiency of farmland operation in hilly areas and realizes to predict how an agricultural robot can transition to the next field after completing its coverage path in the current field. [Methods] In the context of hilly terrain characterized by small and densely distributed field blocks, often separated by field ridges, where there was no clear connectivity between the blocks, a method to establish connectivity between the fields was proposed in the research. This method involved projecting from the corner node of the headland path in the current field to each segment of the headland path in adjacent fields vertically. The shortest projected segment was selected as the candidate connectivity path between the two fields, thus establishing potential connectivity between them. Subsequently, the connectivity was verified, and redundant segments or nodes were removed to further simplify the road network. This method allowed for a more accurate assessment of the actual distances between field blocks, thereby providing a more precise and feasible distance cost between field blocks for multi-block traversal sequence planning. Next, the classical graph algorithm, Floyd algorithm, was employed to address the shortest path problem for all pairs of nodes among the fields. The resulting shortest path matrix among headland path nodes within fields, obtained through the Floyd algorithm, allowed to determine the shortest paths and distances between any two endpoint nodes in different fields. This information was used to ascertain the actual distance cost required for agricultural machinery to transfer between fields. Furthermore, for the genetic algorithm in path planning, there were problems such as difficult parameter setting, slow convergence speed and easy to fall into the local optimal solution. This study improved the traditional genetic algorithm by implementing an adaptive strategy. The improved genetic algorithm in this study dynamically adjusted the crossover and mutation probabilities in each generation based on the fitness of the previous generation, adapting to the problem's characteristics. Simultaneously, it dynamically modified the ratio of parent preservation to offspring generation in the current generation, enhancing population diversity and improving global solution search capabilities. Finally, this study employed genetic algorithms and optimization techniques to address the field traversal order problem, akin to the Traveling Salesman Problem (TSP), with the aim of optimizing the traversal path for agricultural robots. The shortest transfer distances between field blocks obtained through the Floyd algorithm were incorporated as variables into the genetic algorithm for optimization. This process leads to the determination of an optimized sequence for traversing the field blocks and the distribution of entry and exit points for each field block. [Results and Discussions] A traversal path planning simulation experiment was conducted to compare the improved genetic algorithm with the traditional genetic algorithm. After 20 simulation experiments, the average traversal path length and the average convergence iteration count of the two algorithms were compared. The simulation results showed that, compared to the traditional genetic algorithm, the proposed improved genetic algorithm in this study shortened the average shortest path by 13.8%, with fewer iterations for convergence, and demonstrated better capability to escape local optimal solutions. To validate the effectiveness of the multi-field path planning method proposed in this study for agricultural machinery coverage, simulations were conducted using real agricultural field data and field operation parameters. The actual operating area located at coordinates (103.61°E, 30.47°N) was selected as the simulation subject. The operating area consisted of 10 sets of field blocks, with agricultural machinery operating parameters set at a minimum turning radius of 1.5 and a working width of 2. The experimental results showed that in terms of path length and path repetition rate, the present method showed more superior performance, and the field traversal order and the arrangement of imports and exports could effectively reduce the path length and path repetition rate. [Conclusions] The experimental results proved the superiority and feasibility of this study on the traversing path planning of agricultural machines in multiple fields, and the output trajectory coordinates of the algorithm can serve as a reference for both human operators and unmanned agricultural machinery during large-scale operations. In future research, particular attention will be given to addressing practical implementation challenges of intelligent algorithms, especially those related to the real-time aspects of navigation systems and challenges such as Kalman linear filtering. These efforts aim to enhance the applicability of the research findings in real-world scenarios. 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Desert Plant Recognition Method Under Natural Background Incorporating Transfer Learning and Ensemble Learning WANG Yapeng, CAO Shanshan, LI Quansheng, SUN Wei Smart Agriculture    2023, 5 (2): 93-103.   DOI: 10.12133/j.smartag.SA202305001 Abstract （153）   HTML （26）    PDF（pc） （2023KB）（148）       Knowledge map    Save [Objective] Desert vegetation is an indispensable part of desert ecosystems, and its conservation and restoration are crucial. Accurate identification of desert plants is an indispensable task, and is the basis of desert ecological research and conservation. The complex growth environment caused by light, soil, shadow and other vegetation increases the recognition difficulty, and the generalization ability is poor and the recognition accuracy is not guaranteed. The rapid development of modern technology provides new opportunities for plant identification and classification. By using intelligent identification algorithms, field investigators can be effectively assisted in desert plant identification and classification, thus improve efficiency and accuracy, while reduce the associated human and material costs. [Methods] In this research, the following works were carried out for the recognition of desert plant: Firstly, a training dataset of deep learning model of desert plant images in the arid and semi-arid region of Xinjiang was constructed to provide data resources and basic support for the classification and recognition of desert plant images.The desert plant image data was collected in Changji and Tacheng region from the end of September 2021 and July to August 2022, and named DPlants50. The dataset contains 50 plant species in 13 families and 43 genera with a total of 12,507 images, and the number of images for each plant ranges from 183 to 339. Secondly, a migration integration learning-based algorithm for desert plant image recognition was proposed, which could effectively improve the recognition accuracy. Taking the EfficientNet B0-B4 network as the base network, the ImageNet dataset was pre-trained by migration learning, and then an integrated learning strategy was adopted combining Bagging and Stacking, which was divided into two layers. The first layer introduced K-fold cross-validation to divide the dataset and trained K sub-models by borrowing the Stacking method. Considering that the output features of each model were the same in this study, the second layer used Bagging to integrate the output features of the first layer model by voting method, and the difference was that the same sub-models and K sub-models were compared to select the better model, so as to build the integrated model, reduce the model bias and variance, and improve the recognition performance of the model. For 50 types of desert plants, 20% of the data was divided as the test set, and the remaining 5 fold cross validation was used to divide the dataset, then can use DPi(i=1,2,…,5) represents each training or validation set. Based on the pre trained EfficientNet B0-B4 network, training and validation were conducted on 5 data subsets. Finally, the model was integrated using soft voting, hard voting, and weighted voting methods, and tested on the test set. [Results and Discussions] The results showed that the Top-1 accuracy of the single sub-model based on EfficientNet B0 network was 92.26%~93.35%, the accuracy of the Ensemble-Soft model with soft voting, the Ensemble-Hard model with hard voting and the Ensemble-Weight model integrated by weighted voting method were 93.63%, 93.55% and 93.67%, F1 Score and accuracy were comparable, the accuracy and F1 Score of Ensemble-Weight model integrated by weighted voting method were not significantly improved compared with Ensemble-Soft model and Ensemble-hard model, but it showed that the effect of weighted voting method proposed in this study was better than both of them. The three integrated models demonstrate no noteworthy enhancements in accuracy and F1 Score when juxtaposed with the five sub-models. This observation results suggests that the homogeneity among the models constrains the effectiveness of the voting method strategy. Moreover, the recognition effects heavily hinges on the performance of the EfficientNet B0-DP5 model. Therefore, the inclusion of networks with more pronounced differences was considered as sub-models. A single sub-model based on EfficientNet B0-B4 network had the highest Top-1 accuracy of 96.65% and F1 Score of 96.71%, while Ensemble-Soft model, Ensemble-Hard model and Ensemble-Weight model got the accuracy of 99.07%, 98.91% and 99.23%, which further improved the accuracy compared to the single sub-model, and the F1 Score was basically the same as the accuracy rate, and the model performance was significant. The model integrated by the weighted voting method also improved accuracy and F1 Score for both soft and hard voting, with significant model performance and better recognition, again indicating that the weighted voting method was more effective than the other two. Validated on the publicly available dataset Oxford Flowers102, the three integrated models improved the accuracy and F1 Score of the three sub-models compared to the five sub-models by a maximum of 4.56% and 5.05%, and a minimum of 1.94% and 2.29%, which proved that the migration and integration learning strategy proposed in this paper could effectively improve the model performances. [Conclusions] In this study, a method to recognize desert plant images in natural context by integrating migration learning and integration learning was proposed, which could improve the recognition accuracy of desert plants up to 99.23% and provide a solution to the problems of low accuracy, model robustness and weak generalization of plant images in real field environment. After transferring to the server through the cloud, it can realize the accurate recognition of desert plants and serve the scenes of field investigation, teaching science and scientific experiment. 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Visible/NIR Spectral Inversion of Malondialdehyde Content in JUNCAO Based on Deep Convolutional Gengrative Adversarial Network YE Dapeng, CHEN Chen, LI Huilin, LEI Yingxiao, WENG Haiyong, QU Fangfang Smart Agriculture    2023, 5 (3): 132-141.   DOI: 10.12133/j.smartag.SA202307011 Abstract （90）   HTML （16）    PDF（pc） （1784KB）（137）       Knowledge map    Save [Objective] JUNCAO, a perennial herbaceous plant that can be used as medium for cultivating edible and medicinal fungi. It has important value for promotion, but the problem of overwintering needs to be overcome when planting in the temperate zone. Low-temperature stress can adversely impact the growth of JUNCAO plants. Malondialdehyde (MDA) is a degradation product of polyunsaturated fatty acid peroxides, which can serve as a useful diagnostic indicator for studying plant growth dynamics. Because the more severe the damage caused by low temperature stress on plants, the higher their MDA content. Therefore, the detection of MDA content can provide instruct for low-temperature stress diagnosis and JUNCAO plants breeding. With the development of optical sensors and machine learning technologies, visible/near-infrared spectroscopy technology combined with algorithmic models has great potential in rapid, non-destructive and high-throughput inversion of MDA content and evaluation of JUNCAO growth dynamics. [Methods] In this research, six varieties of JUNCAO plants were selected as experimental subjects. They were divided into a control group planted at ambient temperature (28°C) and a stress group planted at low temperature (4°C). The hyperspectral reflectances of JUNCAO seedling leaves during the seedling stage were collected using an ASD spectroradiomete and a near-infrared spectrometer, and then the leaf physiological indicators were measured to obtain leaf MDA content. Machine learning methods were used to establish the MDA content inversion models based on the collected spectral reflectance data. To enhance the prediction accuracy of the model, an improved one-dimensional deep convolutional generative adversarial network (DCAGN ) was proposed to increase the sample size of the training set. Firstly, the original samples were divided into a training set (96 samples) and a prediction set (48 samples) using the Kennard stone (KS) algorithm at a ratio of 2:1. Secondly, the 96 training set samples were generated through the DCGAN model, resulting in a total of 384 pseudo samples that were 4 times larger than the training set. The pseudo samples were randomly shuffled and sequentially added to the training set to form an enhanced modeling set. Finally, the MDA quantitative detection models were established based on random forest (RF), partial least squares regression (PLSR), and convolutional neural network (CNN) algorithms. By comparing the prediction accuracies of the three models after increasing the sample size of the training set, the best MDA regression detection model of JUNCAO was obtained. [Results and Discussions] (1) The MDA content of the six varieties of JUNCAO plants ranged from 12.1988 to 36.7918 nmol/g. Notably, the MDA content of JUNCAO under low-temperature stress was remarkably increased compared to the control group with significant differences (P<0.05). Moreover, the visible/near-infrared spectral reflectance in the stressed group also exhibited an increasing trend compared to the control group. (2) Samples generated by the DCAGN model conformed to the distribution patterns of the original samples. The spectral curves of the generated samples retained the shape and trends of the original data. The corresponding MDA contented of generated samples consistently falling within the range of the original samples, with the average and standard deviation only decreased by 0.6650 and 0.9743 nmol/g, respectively. (3) Prior to the inclusion of generated samples, the detection performance of the three models differed significantly, with a correlation coefficient (R2) of 0.6967 for RF model, that of 0.6729 for CNN model, and that of 0.5298 for the PLSR model. After the introduction of generated samples, as the number of samples increased, all three models exhibited an initial increase followed by a decrease in R2 on the prediction set, while the root mean square error of prediction (RMSEP) first decreased and then increased. (4) The prediction results of the three regression models indicated that augmenting the sample size by using DCGAN could effectively enhance the prediction performance of models. Particularly, utilizing DCGAN in combination with the RF model achieved the optimal MDA content detection performance, with the R2 of 0.7922 and the RMSEP of 2.1937. [Conclusions] Under low temperature stress, the MDA content and spectral reflectance of the six varieties of JUNCAO leaves significantly increased compared to the control group, which might due to the damage of leaf pigments and tissue structure, and the decrease in leaf water content. Augmenting the sample size using DCGAN effectively enhanced the reliability and detection accuracy of the models. This improvement was evident across different regression models, illustrating the robust generalization capabilities of this DCGAN deep learning network. Specifically, the combination of DCGAN and RF model achieved optimal MDA content detection performance, as expanding to a sufficient sample dataset contributed to improve the modeling accuracy and stability. This research provides valuable insights for JUNCAO plants breeding and the diagnosis of low-temperature stress based on spectral technology and machine learning methods, offering a scientific basis for achieving high, stable, and efficient utilization of JUNCAO plants. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （74）   HTML （10）    PDF（pc） （1386KB）（136）       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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Crop Pest Target Detection Algorithm in Complex Scenes:YOLOv8-Extend ZHANG Ronghua, BAI Xue, FAN Jiangchuan Smart Agriculture    DOI: 10.12133/j.smartag.SA202311007 Online available: 04 March 2024

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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 （238）   HTML （44）    PDF（pc） （1179KB）（110）       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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Individual Tree Skeleton Extraction and Crown Prediction Method of Winter Kiwifruit Trees LI Zhengkai, YU Jiahui, PAN Shijia, JIA Zefeng, NIU Zijie Smart Agriculture    2023, 5 (4): 92-104.   DOI: 10.12133/j.smartag.SA202308015 Abstract （70）   HTML （10）    PDF（pc） （2529KB）（110）       Knowledge map    Save [Objective] The proliferation of kiwifruit trees severely overlaps, resulting in a complex canopy structure, rendering it impossible to extract their skeletons or predict their canopies using conventional methods. The objective of this research is to propose a crown segmentation method that integrates skeleton information by optimizing image processing algorithms and developing a new scheme for fusing winter and summer information. In cases where fruit trees are densely distributed, achieving accurate segmentation of fruit tree canopies in orchard drone images can efficiently and cost-effectively obtain canopy information, providing a foundation for determining summer kiwifruit growth size, spatial distribution, and other data. Furthermore, it facilitates the automation and intelligent development of orchard management. [Methods] The 4- to 8-year-old kiwifruit trees were chosen and remote sensing images of winter and summer via unmanned aerial vehicles were obtain as the primary analysis visuals. To tackle the challenge of branch extraction in winter remote sensing images, a convolutional attention mechanism was integrated into the PSP-Net network, along with a joint attention loss function. This was designed to boost the network's focus on branches, enhance the recognition and targeting capabilities of the target area, and ultimately improve the accuracy of semantic segmentation for fruit tree branches.For the generation of the skeleton, digital image processing technology was employed for screening. The discrete information of tree branches was transformed into the skeleton data of a single fruit tree using growth seed points. Subsequently, the semantic segmentation results were optimized through mathematical morphology calculations, enabling smooth connection of the branches. In response to the issue of single tree canopy segmentation in summer, the growth characteristics of kiwifruit trees were taken into account, utilizing the outward expansion of branches growing from the trunk.The growth of tree branches was simulated by using morphological expansion to predict the summer canopy. The canopy prediction results were analyzed under different operators and parameters, and the appropriate expansion operators along with their corresponding operation lengths were selected. The skeleton of a single tree was extracted from summer images. By combining deep learning with mathematical morphology methods through the above steps, the optimized single tree skeleton was used as a prior condition to achieve canopy segmentation. [Results and Discussions] In comparison to traditional methods, the accuracy of extracting kiwifruit tree canopy information images at each stage of the process has been significantly enhanced. The enhanced PSP Net was evaluated using three primary regression metrics: pixel accuracy (PA), mean intersection over union ratio (MIoU), and weighted F1 Score (WF1). The PA, MIoU and WF1 of the improved PSP-Net were 95.84%, 95.76% and 95.69% respectively, which were increased by 12.30%, 22.22% and 17.96% compared with U-Net, and 21.39% , 21.51% and 18.12% compared with traditional PSP-Net, respectively. By implementing this approach, the skeleton extraction function for a single fruit tree was realized, with the predicted PA of the canopy surpassing 95%, an MIoU value of 95.76%, and a WF1 of canopy segmentation approximately at 94.07%.The average segmentation precision of the approach surpassed 95%, noticeably surpassing the original skeleton's 81.5%. The average conformity between the predicted skeleton and the actual summer skeleton stand at 87%, showcasing the method's strong prediction performance. Compared with the original skeleton, the PA, MIoU and WF1 of the optimized skeleton increased by 13.2%, 10.9% and 18.4%, respectively. The continuity of the predicted skeleton had been optimized, resulting in a significant improvement of the canopy segmentation index. The solution effectively addresses the issue of semantic segmentation fracture, and a single tree canopy segmentation scheme that incorporates skeleton information could effectively tackle the problem of single fruit tree canopy segmentation in complex field environments. This provided a novel technical solution for efficient and low-cost orchard fine management. [Conclusions] A method for extracting individual kiwifruit plant skeletons and predicting canopies based on skeleton information was proposed. This demonstrates the enormous potential of drone remote sensing images for fine orchard management from the perspectives of method innovation, data collection, and problem solving. Compared with manual statistics, the overall efficiency and accuracy of kiwifruit skeleton extraction and crown prediction have significantly improved, effectively solving the problem of case segmentation in the crown segmentation process.The issue of semantic segmentation fragmentation has been effectively addressed, resulting in the development of a single tree canopy segmentation method that incorporates skeleton information. This approach can effectively tackle the challenges of single fruit tree canopy segmentation in complex field environments, thereby offering a novel technical solution for efficient and cost-effective orchard fine management. While the research is primarily centered on kiwifruit trees, the methodology possesses strong universality. With appropriate modifications, it can be utilized to monitor canopy changes in other fruit trees, thereby showcasing vast application potential. 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 （96）   HTML （14）    PDF（pc） （2425KB）（109）       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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The Development Logic, Influencing Factors and Realization Path for Low-Carbon Agricultural Mechanization YANG Yinsheng, WEI Xin Smart Agriculture    2023, 5 (4): 150-159.   DOI: 10.12133/j.smartag.SA202304008 Abstract （96）   HTML （15）    PDF（pc） （870KB）（108）       Knowledge map    Save Significance With the escalating global climate change and ecological pollution issues, the "dual carbon" target of Carbon Peak and Carbon Neutrality has been incorporated into various sectors of China's social development. To ensure the green and sustainable development of agriculture, it is imperative to minimize energy consumption and reduce pollution emissions at every stage of agricultural mechanization, meet the diversified needs of agricultural machinery and equipment in the era of intelligent information, and develop low-carbon agricultural mechanization. The development of low-carbon agricultural mechanization is not only an important part of the transformation and upgrading of agricultural mechanization in China but also an objective requirement for the sustainable development of agriculture under the "dual carbon" target. Progress] The connotation and objectives of low-carbon agricultural mechanization are clarified and the development logic of low-carbon agricultural mechanization from three dimensions: theoretical, practical, and systematic are expounded. The "triple-win" of life, production, and ecology is proposed, it is an important criterion for judging the functional realization of low-carbon agricultural mechanization system from a theoretical perspective. The necessity and urgency of low-carbon agricultural mechanization development from a practical perspective is revealed. The "human-machine-environment" system of low-carbon agricultural mechanization development is analyzed and the principles and feasibility of coordinated development of low-carbon agricultural mechanization based on a systemic perspective is explained. Furthermore, the deep-rooted reasons affecting the development of low-carbon agricultural mechanization from six aspects are analyzed: factor conditions, demand conditions, related and supporting industries, production entities, government, and opportunities. Conclusion and Prospects] Four approaches are proposed for the realization of low-carbon agricultural mechanization development: (1) Encouraging enterprises to implement agricultural machinery ecological design and green manufacturing throughout the life cycle through key and core technology research, government policies, and financial support; (2) Guiding agricultural entities to implement clean production operations in agricultural mechanization, including but not limited to innovative models of intensive agricultural land, exploration and promotion of new models of clean production in agricultural mechanization, and the construction of a carbon emission measurement system for agricultural low-carbonization; (3) Strengthening the guidance and implementation of the concept of socialized services for low-carbon agricultural machinery by government departments, constructing and improving a "8S" system of agricultural machinery operation services mainly consisting of Sale, Spare part, Service, Survey, Show, School, Service, and Scrap, to achieve the long-term development of dematerialized agricultural machinery socialized services and green shared operation system; (4) Starting from concept guidance, policy promotion, and financial support, comprehensively advancing the process of low-carbon disposal and green remanufacturing of retired and waste agricultural machinery by government departments. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Phenotype Analysis of Pleurotus Geesteranus Based on Improved Mask R-CNN ZHOU Huamao, WANG Jing, YIN Hua, CHEN Qi Smart Agriculture    2023, 5 (4): 117-126.   DOI: 10.12133/j.smartag.SA202309024 Abstract （83）   HTML （14）    PDF（pc） （1384KB）（108）       Knowledge map    Save [Objective] Pleurotus geesteranus is a rare edible mushroom with a fresh taste and rich nutritional elements, which is popular among consumers. It is not only cherished for its unique palate but also for its abundant nutritional elements. The phenotype of Pleurotus geesteranus is an important determinant of its overall quality, a specific expression of its intrinsic characteristics and its adaptation to various cultivated environments. It is crucial to select varieties with excellent shape, integrity, and resistance to cracking in the breeding process. However, there is still a lack of automated methods to measure these phenotype parameters. The method of manual measurement is not only time-consuming and labor-intensive but also subjective, which lead to inconsistent and inaccurate results. Thus, the traditional approach is unable to meet the demand of the rapid development Pleurotus geesteranus industry. [Methods] To solve the problems which mentioned above, firstly, this study utilized an industrial-grade camera (Daheng MER-500-14GM) and a commonly available smartphone (Redmi K40) to capture high-resolution images in DongSheng mushroom industry (Jiujiang, Jiangxi province). After discarding blurred and repetitive images, a total of 344 images were collected, which included two commonly distinct varieties, specifically Taixiu 57 and Gaoyou 818. A series of data augmentation algorithms, including rotation, flipping, mirroring, and blurring, were employed to construct a comprehensive Pleurotus geesteranus image dataset. At the end, the dataset consisted of 3 440 images and provided a robust foundation for the proposed phenotype recognition model. All images were divided into training and testing sets at a ratio of 8:2, ensuring a balanced distribution for effective model training. In the second part, based upon foundational structure of classical Mask R-CNN, an enhanced version specifically tailored for Pleurotus geesteranus phenotype recognition, aptly named PG-Mask R-CNN (Pleurotus geesteranus-Mask Region-based Convolutional Neural Network) was designed. The PG-Mask R-CNN network was refined through three approaches: 1) To take advantage of the attention mechanism, the SimAM attention mechanism was integrated into the third layer of ResNet101feature extraction network after analyzing and comparing carefully, it was possible to enhance the network's performance without increasing the original network parameters. 2) In order to avoid the problem of Mask R-CNN's feature pyramid path too long to split low-level feature and high-level feature, which may impair the semantic information of the high-level feature and lose the positioning information of the low-level feature, an improved feature pyramid network was used for multiscale fusion, which allowed us to amalgamate information from multiple levels for prediction. 3) To address the limitation of IoU (Intersection over Union) bounding box, which only considered the overlapping area between the prediction box and target box while ignoring the non-overlapping area, a more advanced loss function called GIoU (Generalized Intersection over Union) was introduced. This replacement improved the calculation of image overlap and enhanced the performance of the model. Furthermore, to evaluate crack state of Pleurotus geesteranus more scientifically, reasonably and accurately, the damage rate as a new crack quantification evaluation method was introduced, which was calculated by using the proportion of cracks in the complete pileus of the mushroom and utilized the MRE (Mean Relative Error) to calculate the mean relative error of the Pleurotus geesteranus's damage rate. Thirdly, the PG-Mask R-CNN network was trained and tested based on the Pleurotus geesteranus image dataset. According to the detection and segmentation results, the measurement and accuracy verification were conducted. Finally, considering that it was difficult to determine the ground true of the different shapes of Pleurotus geesteranus, the same method was used to test 4 standard blocks of different specifications, and the rationality of the proposed method was verified. [Results and Discussions] In the comparative analysis, the PG-Mask R-CNN model was superior to Grabcut algorithm and other 4 instance segmentation models, including YOLACT (You Only Look At Coefficien Ts), InstaBoost, QueryInst, and Mask R-CNN. In object detection tasks, the experimental results showed that PG-Mask R-CNN model achieved a mAP of 84.8% and a mAR (mean Average Recall) of 87.7%, respectively, higher than the five methods were mentioned above. Furthermore, the MRE of the instance segmentation results was 0.90%, which was consistently lower than that of other instance segmentation models. In addition, from a model size perspective, the PG-Mask R-CNN model had a parameter count of 51.75 M, which was slightly larger than that of the unimproved Mask R-CNN model but smaller than other instance segmentation models. With the instance segmentation results on the pileus and crack, the MRE were 1.30% and 7.54%, respectively, while the MAE of the measured damage rate was 0.14%. [Conclusions] The proposed PG-Mask R-CNN model demonstrates a high accuracy in identifying and segmenting the stipe, pileus, and cracks of Pleurotus geesteranus. Thus, it can help the automated measurements of phenotype measurements of Pleurotus geesteranus, which lays a technical foundation for subsequent intelligent breeding, smart cultivation and grading of Pleurotus geesteranus. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Low-Cost Chlorophyll Fluorescence Imaging System Applied in Plant Physiology Status Detection YANG Zhenyu, TANG Hao, GE Wei, XIA Qian, TONG Dezhi, FU Lijiang, GUO Ya Smart Agriculture    2023, 5 (3): 154-165.   DOI: 10.12133/j.smartag.SA202306006 Abstract （135）   HTML （20）    PDF（pc） （1735KB）（106）       Knowledge map    Save [Objective] Chlorophyll fluorescence (ChlF) emission from photosystem II (PSII) is closely coupled with photochemical reactions. As an efficient and non-destructive means of obtaining plant photosynthesis efficiency and physiological state information, the collection of fluorescence signals is often used in many fields such as plant physiological research, smart agricultural information sensing, etc. Chlorophyll fluorescence imaging systems, which is the experimental device for collecting the fluorescence signal, have difficulties in application due to their high price and complex structure. In order to solve the issues, this paper investigates and constructs a low-cost chlorophyll fluorescence imaging system based on a micro complementary metal oxide semiconductor (CMOS) camera and a smartphone, and carries out experimental verifications and applications on it. [Method] The chlorophyll fluorescence imaging system is mainly composed of three parts: excitation light, CMOS camera and its control circuit, and a upper computer based on a smartphone. The light source of the excitation light group is based on the principle and characteristics of chlorophyll fluorescence, and uses a blue light source of 460 nm band to achieve the best fluorescence excitation effect. In terms of structure, the principle of integrating sphere was borrowed, the bowl-shaped light source structure was adopted, and the design of the LED surface light source was used to meet the requirements of chlorophyll fluorescence signal measurement for the uniformity of the excitation light field. For the adjustment of light source intensity, the control scheme of pulse width modulation was adopted, which could realize sequential control of different intensities of excitation light. Through the simulation analysis of the light field, the light intensity and distribution characteristics of the light field were stuidied, and the calibration of the excitation light group was completed according to the simulation results. The OV5640 micro CMOS camera was used to collect fluorescence images. Combined with the imaging principle of the CMOS camera, the fluorescence imaging intensity of the CMOS camera was calculated, and its ability to collect chlorophyll fluorescence was analyzed and discussed. The control circuit of the CMOS camera uses an STM32 microcontroller as the microcontroller unit, and completes the data communication between the synchronous light group control circuit and the smartphone through the RS232 to TTL serial communication module and the full-speed universal serial bus, respectively. The smartphone upper computer software is the operating software of the chlorophyll fluorescence imaging system user terminal and the overall control program for fluorescence image acquisition. The overall workflow could be summarized as the user sets the relevant excitation light parameters and camera shooting instructions in the upper computer as needed, sends the instructions to the control circuit through the universal serial bus and serial port, and completes the control of excitation light and CMOS camera image acquisition. After the chlorophyll fluorescence image collection was completed, the data would be sent back to the smart phone or server for analysis, processing, storage, and display. In order to verify the design of the proposed scheme, a prototype of the chlorophyll fluorescence imaging system based on this scheme was made for experimental verification. Firstly, the uniformity of the light field was measured on the excitation light to test the actual performance of the excitation light designed in this article. On this basis, a chlorophyll fluorescence imaging experiment under continuous light excitation and modulated pulse light protocols was completed. Through the analysis and processing of the experimental results and comparison with mainstream chlorophyll fluorometers, the fluorescence imaging capabilities and low-cost advantages of this chlorophyll fluorometer were further verified. [Results and Discussions] The maximum excitation light intensity of the chlorophyll fluorescence imaging system designed in this article was 6250 µmol/(m2·s). Through the simulation analysis of the light field and the calculation and analysis of the fluorescence imaging intensity of the CMOS camera, the feasibility of collecting chlorophyll fluorescence images by the OV5640 micro CMOS camera was demonstrated, which provided a basis for the specific design and implementation of the fluorometer. In terms of hardware circuits, it made full use of the software and hardware advantages of smartphones, and only consisted of the control circuits of the excitation light and CMOS camera and the corresponding communication modules to complete the fluorescence image collection work, simplifying the circuit structure and reducing hardware costs to the greatest extent. The final fluorescence instrument achieved a collection resolution of 5 million pixels, a spectral range of 400~1000 nm, and a stable acquisition frequency of up to 42 f/s. Experimental results showed that the measured data was consistent with theoretical analysis and simulation, which could meet the requirements of fluorescence detection. The instrument was capable of collecting images of chlorophyll fluorescence under continuous light excitation or the protocol of modulated pulsed light. The acquired chlorophyll fluorescence images could reflect the two-dimensional heterogeneity of leaves and could effectively distinguish the photosynthetic characteristics of different leaves. Typical chlorophyll fluorescence parameter images of Fv/Fm, Rfd, etc. were in line with expectations. Compared with the existing chlorophyll fluorescence imaging system, the chlorophyll fluorescence imaging system designed in this article has obvious cost advantages while realizing the rapid detection function of chlorophyll fluorescence. [Conclusions] The instrument is with a simple structure and low cost, and has good application value for the detection of plant physiology and environmental changes. The system is useful for developing other fluorescence instruments. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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An Rapeseed Unmanned Seeding System Based on Cloud-Terminal High Precision Maps LU Bang, DONG Wanjing, DING Youchun, SUN Yang, LI Haopeng, ZHANG Chaoyu Smart Agriculture    2023, 5 (4): 33-44.   DOI: 10.12133/j.smartag.SA202310004 Abstract （95）   HTML （26）    PDF（pc） （2408KB）（102）       Knowledge map    Save [Objective] Unmanned seeding of rapeseed is an important link to construct unmanned rapeseed farm. Aiming at solving the problems of cumbersome manual collection of small and medium-sized field boundary information in the south, the low efficiency of turnaround operation of autonomous tractor, and leaving a large leakage area at the turnaround point, this study proposes to build an unmanned rapeseed seeding operation system based on cloud-terminal high-precision maps, and to improve the efficiency of the turnaround operation and the coverage of the operation. [Methods] The system was mainly divided into two parts: the unmanned seeding control cloud platform for oilseed rape is mainly composed of a path planning module, an operation monitoring module and a real-time control module; the navigation and control platform for rapeseed live broadcasting units is mainly composed of a Case TM1404 tractor, an intelligent seeding and fertilizing machine, an angle sensor, a high-precision Beidou positioning system, an electric steering wheel, a navigation control terminal and an on-board controller terminal. The process of constructing the high-precision map was as follows: determining the operating field, laying the ground control points; collecting the positional data of the ground control points and the orthophoto data from the unmanned aerial vehicle (UAV); processing the image data and constructing the complete map; slicing the map, correcting the deviation and transmitting it to the webpage. The field boundary information was obtained through the high-precision map. The equal spacing reduction algorithm and scanning line filling algorithm was adopted, and the spiral seeding operation path outside the shuttle row was automatically generated. According to the tractor geometry and kinematics model and the size of the distance between the tractor position and the field boundary, the specific parameters of the one-back and two-cut turning model were calculated, and based on the agronomic requirements of rapeseed sowing operation, the one-back-two-cut turn operation control strategy was designed to realize the rapeseed direct seeding unit's sowing operation for the omitted operation area of the field edges and corners. The test included map accuracy test, operation area simulation test and unmanned seeding operation field test. For the map accuracy test, the test field at the edge of Lake Yezhi of Huazhong Agricultural Universit was selected as the test site, where high-precision maps were constructed, and the image and position (POS) data collected by the UAV were processed, synthesized, and sliced, and then corrected for leveling according to the actual coordinates of the correction point and the coordinates of the image. Three rectangular fields of different sizes were selected for the operation area simulation test to compare the operation area and coverage rate of the three operation modes: set row, shuttle row, and shuttle row outer spiral. The Case TM1404 tractor equipped with an intelligent seeding and fertilizer application integrated machine was used as the test platform for the unmanned seeding operation test, and data such as tracking error and operation speed were recorded in real time by software algorithms. The data such as tracking error and operation speed were recorded in real-time. After the flowering of rapeseed, a series of color images of the operation fields were obtained by aerial photography using a drone during the flowering period of rapeseed, and the color images of the operation fields were spliced together, and then the seedling and non-seedling areas were mapped using map surveying and mapping software. [Results and Discussions] The results of the map accuracy test showed that the maximum error of the high-precision map ground verification point was 3.23 cm, and the results of the operation area simulation test showed that the full-coverage path of the helix outside the shuttle row reduced the leakage rate by 18.58%-26.01% compared with that of the shuttle row and the set of row path. The results of unmanned seeding operation field test showed that the average speed of unmanned seeding operation was 1.46 m/s, the maximum lateral deviation was 7.94 cm, and the maximum average absolute deviation was 1.85 cm. The test results in field showed that, the measured field area was 1 018.61 m2, and the total area of the non-growing oilseed rape area was 69.63 m2, with an operating area of 948.98 m2, and an operating coverage rate of 93.16%. [Conclusions] The effectiveness and feasibility of the constructed unmanned seeding operation system for rapeseed were demonstrated. This study can provide technical reference for unmanned seeding operation of rapeseed in small and medium-sized fields in the south. In the future, the unmanned seeding operation mode of rapeseed will be explored in irregular field conditions to further improve the applicability of the system. Table and Figures | 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 （128）   HTML （23）    PDF（pc） （2191KB）（102）       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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Classification and Recognition Method for Yak Meat Parts Based on Improved Residual Network Model ZHU Haipeng, ZHANG Yu'an, LI Huanhuan, WANG Jianwen, YANG Yingkui, SONG Rende Smart Agriculture    2023, 5 (2): 115-125.   DOI: 10.12133/j.smartag.SA202303011 Abstract （178）   HTML （19）    PDF（pc） （1746KB）（95）       Knowledge map    Save [Objective] Conducting research on the recognition of yak meat parts can help avoid confusion and substandard parts during the production and sales of yak meat, improve the transparency and traceability of the yak meat industry, and ensure food safety. To achieve fast and accurate recognition of different parts of yak meat, this study proposed an improved residual network model and developed a smartphone based yak meat part recognition software. [Methods] Firstly, the original data set of 1960 yak tenderloin, high rib, shank and brisket were expanded by 8 different data enhancement methods, including horizontal flip, vertical flip, random direction rotation 30°, random direction rotation 120°, random direction rotation 300°, contrast adjustment, saturation adjustment and hue adjustment. After expansion, 17,640 yak meat images of different parts were obtained. The expanded yak meat images of different parts were divided according to the 4:1 ratio, resulting in 14,112 yak meat sample images in the training set and 3528 yak meat sample images in the test set. Secondly, the convolutional block attention module (CBAM) was integrated into each residual block of the original network model to enhance the extraction of key detail features of yak images in different parts. At the same time, introducing this mechanism into the network model could achieve greater accuracy improvement with less computational overhead and fewer parameters. In addition, in the original network model, the full connection layer was directly added after all residual blocks instead of global average pooling and global maximum pooling, which could improve the accuracy of the network model, prevent overfitting, reduce the number of connections in subsequent network layers, accelerate the execution speed of the network model, and reduce the computing time when the mobile phone recognized images. Thirdly, different learning rates, weight attenuation coefficients and optimizers were used to verify the influence of the improved ResNet18_CBAM network model on convergence speed and accuracy. According to the experiments, the stochastic gradient descent (SGD) algorithm was adopted as the optimizer, and when the learning rate was 0.001 and the weight attenuation coefficient was 0, the improved ReaNet18_CBAM network model had the fastest convergence speed and the highest recognition accuracy on different parts of yak data sets. Finally, the PyTorch Mobile module in PyTorch deep learning framework was used to convert the trained ResNet18_CBAM network model into TorchScript model and saved it in *.ptl. Then, the yak part recognition App was developed using the Android Studio development environment, which included two parts: Front-end interface and back-end processing. The front-end of the App uses *.xml for a variety of price control layout, and the back-end used Java language development. Then TorchScript model in *.ptl was used to identify different parts of yak meat. Results and Discussions] In this study, CBAM, SENet, NAM and SKNet, four popular attentional mechanism modules, were integrated into the original ResNet18 network model and compared by ablation experiments. Their recognition accuracy on different parts of yak meat dataset were 96.31%, 94.12%, 92.51% and 93.85%, respectively. The results showed that among CBAM, SENet, NAM and SKNet, the recognition accuracy of ResNet18 CBAM network model was significantly higher than that of the other three attention mechanism modules. Therefore, the CBAM attention mechanism module was chosen as the improvement module of the original network model. The accuracy of the improved ResNet18_CBAM network model in the test set of 4 different parts of yak tenderloin, high rib, shank and brisket was 96.31%, which was 2.88% higher than the original network model. The recognition accuracy of the improved ResNet18_CBAM network model was compared with AlexNet, VGG11, ResNet34 and ResNet18 network models on different parts of yak test set. The improved ResNet18_CBAM network model had the highest accuracy. In order to verify the actual results of the improved ResNet18_CBAM network model on mobile phones, the test conducted in Xining beef and mutton wholesale market. In the actual scenario testing on the mobile end, a total of 54, 59, 51, and 57 yak tenderloin, high rib, shank and brisket samples were collected, respectively. The number of correctly identified samples and the number of incorrectly identified samples were counted respectively. Finally, the recognition accuracy of tenderloin, high rib, shank and brisket of yak reached 96.30%, 94.92%, 98.04% and 96.49%, respectively. The results showed that the improved ResNet18_CBAM network model could be used in practical applications for identifying different parts of yak meat and has achieved good results. [Conclusions] The research results can help ensure the food quality and safety of the yak industry, improve the quality and safety level of the yak industry, improve the yak trade efficiency, reduce the cost, and provide technical support for the intelligent development of the yak industry in the Qinghai-Tibet Plateau region. 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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 （65）   HTML （13）    PDF（pc） （1719KB）（82）       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 （106）   HTML （23）    PDF（pc） （2011KB）（81）       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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Research Progresses of Crop Growth Monitoring Based on Synthetic Aperture Radar Data HONG Yujiao, ZHANG Shuo, LI Li Smart Agriculture    2024, 6 (1): 46-62.   DOI: 10.12133/j.smartag.SA202308019 Abstract （39）   HTML （10）    PDF（pc） （1147KB）（64）       Knowledge map    Save Significance Crop production is related to national food security, economic development and social stability, so timely information on the growth of major crops is of great significance for strengthening the crop production management and ensuring food security. The traditional crop growth monitoring mainly judges the growth of crops by manually observing the shape, color and other appearance characteristics of crops through the external industry, which has better reliability and authenticity, but it will consume a lot of manpower, is inefficient and difficult to carry out monitoring of a large area. With the development of space technology, satellite remote sensing technology provides an opportunity for large area crop growth monitoring. However, the acquisition of optical remote sensing data is often limited by the weather during the peak crop growth season when rain and heat coincide. Synthetic aperture radar (SAR) compensates well for the shortcomings of optical remote sensing, and has a wide demand and great potential for application in crop growth monitoring. However, the current research on crop growth monitoring using SAR data is still relatively small and lacks systematic sorting and summarization. In this paper, the research progress of SAR inversion of crop growth parameters were summarized through comprehensive analysis of existing literature, clarify the main technical methods and application of SAR monitoring of crop growth, and explore the existing problems and look forward to its future research direction. Progress] The current research status of SAR crop growth monitoring were reviewed, the application of SAR technology had gone through several development stages: from the early single-polarization, single-band stage, gradually evolving to the mid-term multi-polarization, multi-band stage, and then to the stage of joint application of tight polarization and optical remote sensing. Then, the research progress and milestone achievements of crop growth monitoring based on SAR data were summarized in three aspects, namely, crop growth SAR remote sensing monitoring indexes, crop growth SAR remote sensing monitoring data and crop growth SAR remote sensing monitoring methods. First, the key parameters of crop growth were summarized, and the crop growth monitoring indexes were divided into morphological indicators, physiological and biochemical indicators, yield indicators and stress indicators. Secondly, the core principle of SAR monitoring of crop growth parameters was introduced, which was based on the interaction between SAR signals and vegetation, and then the specific scattering model and inversion algorithm were used to estimate the crop growth parameters. Then, a detailed summary and analysis of the radar indicators mainly applied to crop growth monitoring were also presented. Finally, SAR remote sensing methods for crop growth monitoring, including mechanistic modeling, empirical modeling, semi-empirical modeling, direct monitoring, and assimilation monitoring of crop growth models, were described, and their applicability and applications in growth monitoring were analyzed. Conclusions and Prospects Four challenges exist in SAR crop growth monitoring are proposed: 1) Compared with the methods of crop growth monitoring using optical remote sensing data, the methods of crop growth monitoring using SAR data are obviously relatively small. The reason may be that SAR remote sensing itself has some inherent shortcomings; 2) Insufficient mining of microwave scattering characteristics, at present, a large number of studies have applied the backward scattering intensity and polarization characteristics to crop growth monitoring, but few have applied the phase information to crop growth monitoring, especially the application study of polarization decomposition parameters to growth monitoring. The research on the application of polarization decomposition parameter to crop growth monitoring is still to be deepened; 3) Compared with the optical vegetation index, the radar vegetation index applied to crop growth monitoring is relatively less; 4 ) Crop growth monitoring based on SAR scattered intensity is mainly based on an empirical model, which is difficult to be extended to different regions and types of crops, and the existence of this limitation prevents the SAR scattering intensity-based technology from effectively realizing its potential in crop growth monitoring. Finally, future research should focus on mining microwave scattering features, utilizing SAR polarization decomposition parameters, developing and optimizing radar vegetation indices, and deepening scattering models for crop growth monitoring. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Ecological Risk Assessment of Cultivated Land Based on Landscape Pattern: A Case Study of Tongnan District, Chongqing ZHANG Xingshan, YANG Heng, MA Wenqiu, YANG Minli, WANG Haiyi, YOU Yong, HUI Yunting, GONG Zeqi, WANG Tianyi Smart Agriculture    DOI: 10.12133/j.smartag.SA202306008 Online available: 20 December 2023

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Contactless Conductivity Microfluidic Chip for Rapid Determination of Soil Nitrogen and Potassium Content HONG Yan, WANG Le, WANG Rujing, SU Jingming, LI Hao, ZHANG Jiabao, GUO Hongyan, CHEN Xiangyu Smart Agriculture    2024, 6 (1): 18-27.   DOI: 10.12133/j.smartag.SA202309022 Abstract （68）   HTML （12）    PDF（pc） （1344KB）（52）       Knowledge map    Save Objective The content of nitrogen (N) and potassium (K) in the soil directly affects crop yield, making it a crucial indicator in agricultural production processes. Insufficient levels of the two nutrients can impede crop growth and reduce yield, while excessive levels can result in environmental pollution. Rapidly quantifying the N and K content in soil is of great importance for agricultural production and environmental protection. Methods A rapid and quantitative method was proposed for detecting N and K nutrient ions in soil based on polydimethylsiloxane (PDMS) microfluidic chip electrophoresis and capacitively coupled contactless conductivity detection (C4D). Microfluidic chip electrophoresis enables rapid separation of multiple ions in soil. The electrophoresis microfluidic chips have a cross-shaped channel layout and were fabricated using soft lithography technology. The sample was introduced into the microfluidic chip by applying the appropriate injection voltage at both ends of the injection channel. This simple and efficient procedure ensured an accurate sample introduction. Subsequently, an electrophoretic voltage was applied at both ends of the separation channel, creating a capillary zone electrophoresis that enables the rapid separation of different ions. This process offered high separation efficiency, required a short processing time, and had a small sample volume requirement. This enabled the rapid processing and analysis of many samples. C4D enabled precise measurement of changes in conductivity. The sensing electrodes were separated from the microfluidic chips and printed onto a printed circuit board (PCB) using an immersion gold process. The ions separated under the action of an electric field and sequentially reach the sensing electrodes. The detection circuit, connected to the sensing electrodes, received and regulated the conductivity signal to reflect the variance in conductivity between the sample and the buffer solution. The sensing electrodes were isolated from the sample solution to prevent interference from the high-voltage electric field used for electrophoresis. Results and Discussions The voltage used for electrophoresis, as well as the operating frequency and excitation voltage of the excitation signal in the detection system, had a significant effect on separation and detection performance. Based on the response characteristics of the system output, the optimal operating frequency of 1 000 kHz, excitation voltage of 50 V, and electrophoresis voltage of 1.5 kV were determined. A peak overshoot was observed in the electrophoresis spectrum, which was associated with the operating frequency of the system. The total noise level of the system was approximately 0.091 mV. The detection limit (S/N = 3) for soil nutrient ions was determined by analyzing a series of standard sample solutions with varying concentrations. The detection limited for potassium (K+), ammonium (NH4+), and nitrate (NO3‒) standard solutions were 0.5, 0.1 and 0.4 mg/L, respectively. For the quantitative determination of soil nutrient ion concentration, the linear relationship between peak area and corresponding concentration was investigated under optimal experimental conditions. K+, NH4+, and NO3‒ exhibit a strong linear relationship in the range of 0.5~40 mg/L, with linear correlation coefficients (R2) of 0.994, 0.997, and 0.990, respectively, indicating that this method could accurately quantify N and K ions in soil. At the same time, to evaluate the repeatability of the system, peak height, peak area, and peak time were used as evaluation indicators in repeatability experiments. The relative standard deviation (RSD) was less than 4.4%, indicating that the method shows good repeatability. In addition, to assess the ability of the C4D microfluidic system to detect actual soil samples, four collected soil samples were tested using MES/His and PVP/PTAE as running buffers. K+, NH4+,Na+, Chloride (Cl‒), NO3‒, and sulfate (SO43‒) were separated sequentially within 1 min. The detection efficiency was significantly improved. To evaluate the accuracy of this method, spiked recovery experiments were performed on four soil samples. The recovery rates ranged from 81.74% to 127.76%, indicating the good accuracy of the method. Conclusions This study provides a simple and effective method for the rapid detection of N and K nutrient ions in soil. The method is highly accurate and reliable, and it can quickly and efficiently detect the contents of N and K nutrient ions in soil. This contactless measurement method reduced costs and improved economic efficiency while extending the service life of the sensing electrodes and reducing the frequency of maintenance and replacement. It provided strong support for long-term, continuous conductivity monitoring. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Automated Flax Seeds Testing Methods Based on Machine Vision MAO Yongwen, HAN Junying, LIU Chengzhong Smart Agriculture    2024, 6 (1): 135-146.   DOI: 10.12133/j.smartag.SA202309011 Abstract （50）   HTML （21）    PDF（pc） （1671KB）（52）       Knowledge map    Save Objective Flax, characterized by its short growth cycle and strong adaptability, is one of the major cash crops in northern China. Due to its versatile uses and unique quality, it holds a significant position in China's oil and fiber crops. The quality of flax seeds directly affects the yield of the flax plant. Seed evaluation is a crucial step in the breeding process of flax. Common parameters used in the seed evaluation process of flax include circumference, area, length axis, and 1 000-seed weight. To ensure the high-quality production of flax crops, it is of great significance to understand the phenotypic characteristics of flax seeds, select different resources as parents based on breeding objectives, and adopt other methods for the breeding, cultivation, and evaluation of seed quality and traits of flax. Methods In response to the high error rates and low efficiency issues observed during the automated seed testing of flax seeds, the measurement methods were explored of flax seed contours based on machine vision research. The flax seed images were preprocessed, and the collected color images were converted to grayscale. A filtering and smoothing process was applied to obtain binary images. To address the issues of flax seed overlap and adhesion, a contour fitting image segmentation method based on fused corner features was proposed. This method incorporated adaptive threshold selection during edge detection of the image contour. Only multi-seed target areas that met certain criteria were subjected to image segmentation processing, while single-seed areas bypassed this step and were directly summarized for seed testing data. After obtaining the multi-seed adhesion target areas, the flax seeds underwent contour approximation, corner extraction, and contour fitting. Based on the provided image contour information, the image contour shape was approximated to another contour shape with fewer vertices, and the original contour curve was simplified to a more regular and compact line segment or polygon, minimizing computational complexity. All line shape characteristics in the image were marked as much as possible. Since the pixel intensity variations in different directions of image corners were significant, the second derivative matrix based on pixel grayscale values was used to detect image corners. Based on the contour approximation algorithm, contour corner detection was performed to obtain the coordinates of each corner. The resulting contour points and corners were used as outputs to further improve the accuracy and precision of subsequent contour fitting methods, resulting in a two-dimensional discrete point dataset of the image contour. Using the contour point dataset as an input, the geometric moments of the image contour were calculated, and the optimal solution for the ellipse parameters was obtained through numerical optimization based on the least squares method and the geometric features of the ellipse shape. Ultimately, the optimal contour was fitted to the given image, achieving the segmentation and counting of flax seed images. Meanwhile, each pixel in the digital image was a uniform small square in size and shape, so the circumference, area, and major and minor axes of the flax seeds could be represented by the total number of pixels occupied by the seeds in the image. The weight of a single seed could be calculated by dividing the total weight of the seeds by the total number of seeds detected by the contour, thereby obtaining the weight of the individual seed and converting it accordingly. Through the pixelization of the 1 yuan and 1 jiao coins from the fifth iteration of the 2019 Renminbi, a summary of the circumference, area, major axis, minor axis, and 1 000-seed weight of the flax seeds was achieved. Additionally, based on the aforementioned method, this study designed an automated real-time analysis system for flax seed testing data, realizing the automation of flax seed testing research. Experiments were conducted on images of flax seeds captured by an industrial camera. Results and Discussions The proposed automated seed identification method achieved an accuracy rate of 97.28% for statistically distinguishing different varieties of flax seeds. The average processing time for 100 seeds was 69.58 ms. Compared to the extreme erosion algorithm and the watershed algorithm based on distance transformation, the proposed method improved the average calculation accuracy by 19.6% over the extreme erosion algorithm and required a shorter average computation time than the direct use of the watershed algorithm. Considering the practical needs of automated seed identification, this method did not employ methods such as dilation or erosion for image morphology processing, thereby preserving the original features of the image to the greatest extent possible. Additionally, the flax seed automated seed identification data real-time analysis system could process image information in batches. By executing data summarization functions, it automatically generated corresponding data table folders, storing the corresponding image data summary tables. Conclusions The proposed method exhibits superior computational accuracy and processing speed, with shorter operation time and robustness. It is highly adaptable and able to accurately acquire the morphological feature parameters of flax seeds in bulk, ensuring measurement errors remain within 10%, which could provide technical support for future flax seed evaluation and related industrial development. 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 （62）   HTML （11）    PDF（pc） （1068KB）（47）       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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Identification Method of Kale Leaf Ball Based on Improved UperNet ZHU Yiping, WU Huarui, GUO Wang, WU Xiaoyan Smart Agriculture    DOI: 10.12133/j.smartag.SA202401020 Online available: 08 March 2024

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Intelligent Detection and Alarm System for Ferrous Metal Foreign Objects in Silage Machines ZHANG Qing, LI Yang, YOU Yong, WANG Decheng, HUI Yunting Smart Agriculture    2024, 6 (1): 111-122.   DOI: 10.12133/j.smartag.SA202306010 Abstract （44）   HTML （6）    PDF（pc） （2026KB）（43）       Knowledge map    Save Objective During the operation of the silage machine, the inclusion of ferrous metal foreign objects such as stray iron wires can inflict severe damage to the machine's critical components and livestock organs. To safeguard against that, a metal detection system with superior performance was developed in this research to enable precise and efficient identification of metal foreign bodies during field operations, ensuring the integrity of the silage process and the well-being of the animals. Methods The ferrous metal detection principle of silage machine was firstly analyzed. The detection coil is the probe of the metal detection system. After being connected in parallel with a capacitor, it is connected to the detection module. The detection coil received the alternating signal generated by the detection module to generate an alternating magnetic field. After the metal object entered the magnetic field, it affects the equivalent resistance and equivalent inductance of the detection coil. The detection module detected the change of the equivalent resistance and equivalent inductance, and then transmited the signal to the control module through the serial peripheral interface (SPI). The control module filtered the signal and transmited it to the display terminal through the serial port. The display terminal could set the threshold. When the data exceeded the threshold, the system performed sound and light alarm and other processing. Hardware part of the metal detection system of silage machine were firstly design. The calculation of the planar spiral coil and the cylindrical coil was carried out and the planar spiral coil was selected as the research object. By using the nondominated sorting genetic algorithm-Ⅱ (NSGA-II) combined with the method of finite element simulation analysis, the wire diameter, inner diameter, outer diameter, layer number and frequency of the coil were determined, and the calculation of the bent coil and the unbent coil and the array coil was carried out. The hardware system was integrated. The software system for the metal detection system was also designed, utilizing an STM32 microcontroller as the control module and LabView for writing the primary program on the upper computer. The system continuously displayed the read data and time-equivalent impedance graph in real-time, allowing for the setting of upper and lower alarm thresholds. When a metal foreign object was detected, the warning light turned red and an alarm sound was emitted, causing the feed roll to stop. To simulate the scenario of metal detection during the operation of a silage machine, a test bench was set up to validate the performance of the metal detection system. Results and Discussions The test results of the metal detection function showed that for a metal wire with a diameter of 0.6 mm and a length of 20 mm, as the inner diameter of the detection coil increased, the maximum alarm distance increased first and then decreased. The maximum alarm distance occured when the inner diameter was 35 mm, which was consistent with the optimization result. The maximum alarm distance was the largest when the detection coil was two layers, and there was no data readout when it was three layers. Therefore, the optimal thickness of the detection coil for this metal detection system was two layers. When the detection distance was greater than 80 mm, the alarm rate began to decrease, and the detection effect was weakened. When the detection distance was within 70 mm, the metal detection system could achieve a 100% alarm rate. The test results of the system response time showed that the average system response time was 0.105 0 s, which was less than the safe transportation time of 0.202 0 s. The system can give an alarm before the metal foreign object reaches the cutter, so the system is safe and effective. Conclusion In this study, a metal detection system for silage machines was designed. A set of optimization methods for metal detection coils was proposed, and the corresponding metal detection software and hardware systems were developed, and the functions of the metal detection system were verified through experiments, which could provide strong technical support for the safe operation of silage machines. 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 （32）   HTML （7）    PDF（pc） （1503KB）（40）       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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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 （43）   HTML （6）    PDF（pc） （1819KB）（39）       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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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 （32）   HTML （6）    PDF（pc） （1373KB）（38）       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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Suitable Sowing Date for Winter Wheat Based on European Centre for Medium-Range Weather Forecasts Reanalysis Data: A Case Study of Qihe County, Shandong Province LIU Ruixuan, ZHANG Fangzhao, ZHANG Jibo, LI Zhenhai, YANG Juntao Smart Agriculture    DOI: 10.12133/j.smartag.SA202309019 Online available: 26 January 2024

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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    DOI: 10.12133/j.smartag.SA201311014 Online available: 29 February 2024

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Automatic Identification Method for Spectral Peaks of Soil Nutrient Ions Using Contactless Conductivity Detection TANG Chaoli, LI Hao, WANG Rujing, WANG Le, HUANG Qing, WANG Dapeng, ZHANG Jiabao, CHEN Xiangyu Smart Agriculture    2024, 6 (1): 36-45.   DOI: 10.12133/j.smartag.SA202309028 Abstract （25）   HTML （3）    PDF（pc） （2822KB）（35）       Knowledge map    Save Objective Capacitive coupled contactless conductivity detection (C4D) plays an important role in agricultural soil nutrient ion detection. Effective identification of characteristic ion peaks in C4D signals is conducive to subsequent qualitative and quantitative analysis of characteristic ion peaks, which provides a basis for improving agricultural soil nutrient management. However, the detection of characteristic peaks in C4D signals still has shortcomings, such as the inability of automatic and accurate identification, complicated manual operation, and low efficiency. Methods In this study, an automatic spectral peak identification algorithm based on continuous wavelet transform combined with particle swarm optimization (PSO) and maximum interclass variance method (Otsu) was proposed to achieve accurate, efficient and automated identification of C4D signal peaks. Capillary electrophoresis (CE) combined with a C4D device (CE-C4D) was used to detect the standard ions and soil sample solutions to obtain the C4D ion signal spectra, which were simulated according to the characteristics of the real C4D signal spectra to obtain the C4D simulated signals containing single Gaussian peaks and multi-Gaussian peaks. The continuous wavelet transform was performed on the C4D spectrogram signal to obtain the wavelet transform coefficient matrix. The local maxima and local minima of the continuous wavelet transform coefficient matrix were searched by the staircase scanning method, and the local maxima and local minima were connected to form ridges and valleys. The wavelet coefficient matrix was converted to a gray-scale image by logistic mapping to visualize the data. The number of particle populations in PSO was set to 15, the gray scale threshold of 15 particles was set to a random integer within the gray scale level of 0~255, and the initial velocity of the particles was set to 5. The combination of PSO and Otsu calculated the fitness (variance value) of each particle, updated the individual best position and the global best position, further updated the velocities and positions of the particles to find the global best position (the maximum interclass variance), and defined the maximum interclass variance was defined as the optimal threshold value, used the optimal threshold value for background and target segmentation of the gray-scale image and extracted the ridges within the peak region segmented from the gray-scale image by the PSO-Otsu algorithm. A threshold was set according to the length of the ridge line; the ridge lines larger than the threshold were extracted; the valley lines on both sides of the ridge line were found according to the filtered ridge line; and the start and end points of the peak region were obtained from the valley lines. The filtered ridge lines were used to identify the peak location of the peak region, and the edge threshold was set to remove the false peaks due to continuous wavelet transform (CWT) located in the edge region of the C4D signal and to accurately identify the location of the true peak value. Results and Discussions The datasets containing 41, 61 and 102 peaks were tested, and the Receiver Operating Characteristic (ROC) curves and metric values were used as a guideline to evaluate the performance of the peak detection algorithms. Compared to the two methods, multi scale peak detection (MSPD) and CWT-based image segmentation (CWT-IS), the CWT combined with Particle Swarm Optimization based maximum spectral peaks automatic identification algorithm based on Continuous Wavelet Transform combined with Particle Swarm Optimization of Otsu to segment image (CWTSPSO) method of interclass variance segmentation (CWT-IS), the ROC curves of the three groups remained above 0.9. Testing the dataset containing 102 peaks, the ROC curves of MSPD and CWT-IS were also high only in the case of high false discovery rate. The highest metric values of CWTSPSO were 0.976, 0.915, and 0.969, respectively, and the highest metric values of 1 set of MSPD and CWT-IS were 0.754 and 0.505. The results showed that the ROC curves of CWTSPSO in the 3 sets of dataset were not high. Using ROC curves and metric values as a criterion comparison to evaluate the performance of peak detection algorithms, the characteristic peak recognition performance was outstanding, which could achieve a higher correct rate while maintaining a lower false discovery rate, effectively detected more weak and overlapping peaks while detecting fewer false peaks, which was conducive to the enhancement of the spectral peak recognition rate and accuracy of the C4D signals. Conclusions This study provided a fast and accurate method for the identification of characteristic peaks in the spectrograms of ion signals detected by contactless conductivity, CWTSPSO could accurately identify the weak and overlapping peaks in the spectrograms of ion signals detected by contactless conductivity. The automatic identification algorithm of the spectrogram peaks of CWTSPSO would provide powerful support for the characterization and quantification of the signals of nutrient ions detected by contactless conductivity in agricultural soils and would further strengthen the precision of soil fertilization and crop growth management fertilization and crop growth management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Remote Sensing Identification of Cultivated Land at Hill County of Sichuan Basin Based on Deep Learning LI Hao, DU Yuqiu, XIAO Xingzhu, CHEN Yanxi Smart Agriculture    DOI: 10.12133/j.smartag.SA202308002 Online available: 26 January 2024

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Electrochemical Immunosensor for in Situ Detection of Brassinolide WEI Qian, GAO Yuanyuan, LI Aixue Smart Agriculture    2024, 6 (1): 76-88.   DOI: 10.12133/j.smartag.SA202311001 Abstract （21）   HTML （2）    PDF（pc） （1903KB）（26）       Knowledge map    Save Objective Brassinolide is an important endogenous plant hormone. In this work, an electrochemical immunosensor for in situ detection of brassinolide was constructed using screen-printed electrode (SPE). Methods Au nanoparticles (AuNPs) were firstly electrodeposited on the surface of SPE electrode by electrochemical workstation, and then CuCl2 nanowires (CuCl2 NWs) were added to the electrode, which can not only increase the conductivity of the electrode, but also Cu2+ can be used as a REDOX probe for the sensor. Finally, Mxene and polydopamine nanocomposite (Mxene@PDA) were selected as the modification materials for SPE electrodes because Mxene has the advantages of large surface area and good electrical conductivity, which can further amplify Cu2+ signals. However, Mxene is easily oxidized and unstable in air. Polydopamine (PDA) contains a large number of catechol and amino groups, which are coated on the surface of Mxene after self-polymerization by dopamine, cutting off the path of oxygen penetration, making Mxene difficult to be oxidized. Mxene@PDA can also be used as a coupling agent to fix more antibodies on the electrode surface, improving the overall biocompatibility, and improve the overall biocompatibility. Results and Discussions The sensor has a wide linear detection range: 0.1 pg/mL to 1 mg/mL, and the detection limit was 0.015 pg/ml (S/N=3). In addition, the content of endogenous brassinolide in wheat was detected by SPE electrodes in vitro and the recovery rate was 98.13% to 104.74%.While verifying the accuracy of the sensor, it also demonstrated its superior stability and sensitivity. Besides, the sensor also showed excellent application potential in the in situ detection of brassinosteroids from wheat leaves. Compared with other brassinolide detection methods, the immunosensor developed in this study has better analytical performance. Conclusions An electrochemical immunosensor for in situ detection of brassinolide was developed for the first time, providing a good electrochemical platform for in situ determination of brassinolide in plant leaves, which has great application potential in precision 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 （25）   HTML （2）    PDF（pc） （2050KB）（24）       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）

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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    DOI: 10.12133/j.smartag.SA202309006 Online available: 05 February 2024

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Three-dimensional Dynamic Growth and Yield Simulation of Daylily Plants Based on Source-Sink Relationships ZHANG Yue, LI Weijia, HAN Zhiping, ZHANG Kun, LIU Jiawen, HENKE Michael Smart Agriculture    DOI: 10.12133/j.smartag.SA202310011 Online available: 06 February 2024

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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    DOI: 10.12133/j.smartag.SA202312028 Online available: 06 March 2024

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Agricultural Disease Named Entity Recognition with Pointer Network Based on RoFormer Pre-trained Model WANG Tong, WANG Chunshan, LI Jiuxi, ZHU Huaji, MIAO Yisheng, WU Huarui Smart Agriculture    DOI: 10.12133/j.smartag.SA202311021 Online available: 05 March 2024

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A Regional Farming Pig Counting System Based on Improved Instance Segmentation Algorithm ZHANG Yanqi, ZHOU Shuo, ZHANG Ning, CHAI Xiujuan, SUN Tan Smart Agriculture    DOI: 10.12133/j.smartag.SA202310001 Online available: 28 February 2024

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Research on Cabbage Transplant Status Detection Based on Improved YOLOv8s WU Xiaoyan, GUO Wei, ZHU Yiping, ZHU Huaji, WU Huarui Smart Agriculture    DOI: 10.12133/j.smartag.SA202401008 Online available: 06 March 2024

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Detection of Oilseed Rape Sclerotinia in Hyperspectral Images Based on Bi-GRU and Spatial-Spectral Information ZHANG Jing, ZHAO Zexuan, ZHAO Yanru, BU Hongchao, WU Xingyu Smart Agriculture    DOI: 10.12133/j.smartag.SA202310010 Online available: 05 March 2024

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AgNPs Prepared by Lemon Juice Reduction Method for SERS Rapid Detection of Pesticide Residues in Fruits and Vegetables DONG Shanshan, ZHANG Fengqiu, XIA Qi, LI Jialin, LIU Chao, LIU Shaowei, CHEN Xiangyu, WANG Rujing, HUANG Qing Smart Agriculture    2024, 6 (1): 101-110.   DOI: 10.12133/j.smartag.SA202311010 Abstract （13）   HTML （1）    PDF（pc） （2305KB）（5）       Knowledge map    Save Objective The use of pesticides is one of the root causes of food safety problems. Pesticide exposure and pesticide residues can not only lead to environmental pollution issues but also seriously affect human health. In order to meet the rapid and sensitive detection needs of pesticide residues in agricultural products, a method based on lemon juice reduction to prepare silver nanoparticles (AgNPs) is reported in this research. Methods First, fresh lemon juice was filtered through filter paper and diluted to a 2% lemon juice aqueous solution. Then, a certain concentration of AgNO3 solution, 50 mm NaOH solution were prepared and stored at room temperature. Then, 10 mL ddH2O, 2 mL NaOH, 2 mL 2% lemon juice, and 5 mL AgNO3 solution were mixed. When the solution turned to a clear yellow color, the solution was centrifuged to obtain AgNPs. The morphology and structure of AgNPs were observed by transmission electron microscopy (TEM). In order to verify the successful synthesis of the nanoparticles and the distribution characteristics of the nanoparticles, ultraviolet spectroscopy was used for measurement and analysis, and 4-ATP was used as a SERS probe to preliminarily verify the SERS enhancement performance of AgNPs. Furthermore, the content of the main reducing components in lemon juice, namely ascorbic acid, glucose, and fructose was analyzed. The content of ascorbic acid in lemon juice was determined by high-performance liquid chromatography, and the content of glucose and fructose in lemon juice was determined by UV-visible spectrophotometry. To verify the stability and uniformity of the SERS signal of the nanoparticles, 4-ATP was used as an surface enhancement of raman scattering (SERS) probe for detection analysis. The stability of the SERS performance of the colloidal substrate within 41 days and the SERS performance at temperatures ranging from 0-80 °C were analyzed. Using 4-ATP as the SERS probe, the experimental conditions were optimized for the preparation of AgNPs by the lemon juice method, including pH and AgNO3 concentration. To validate the practical usability of the nanoparticles, the solutions of paraquat and carbendazim and the detection limits of pesticide residues on different fruits and vegetables were detected by SERS. Results and discussions The method for preparing AgNPs has the advantages of simple operation, green and easy synthesis. The particle morphology and size of the prepared AgNPs were basically uniform, with a size of about 20 nm. The ultraviolet-visible spectrum of AgNPs solution showed that the absorption peak was about 400 nm and the peak shape was narrow, indicating that the colloidal solution had good homogeneity. The detection limit of 4-ATP as the SERS probe was 10-14 M, indicating that the nanoparticle had a good SERS. In addition, the content of ascorbic acid, the main reducing ingredient, in lemon juice measured by high-performance liquid chromatography (HPLC) was 395.76 μg/mL. The contents of glucose and fructose, which were the main reducing components in lemon juice, were 5.95 and 5.90 mg/mL, respectively. Furthermore, the characterization and analysis results of the AgNPs prepared by the mixed reducing solution prepared according to the concentration data of each component showed that the AgNPs obtained were also uniform in morphology and size, with a diameter of about 20 nm, but the SERS enhancement performance was not as good as that of the AgNPs reduced by lemon juice. The SERS signal uniformity of the AgNPs reduced by lemon juice analyzed results showed that the peak intensity of the SERS spectral of 4-ATP at different sites at the same concentration was not significantly changed for 15 times, and its standard deviation RSD=5.03%, which was much lower than the intersubstrate RSD value (<16%) of the qualified new SERS active substrate for quantitative analysis. The temporal stability and temperature stability of AgNPs analyzed results showed that the nanoparticles still had SERS enhanced performance after 41 days of storage, and had SERS enhanced performance stability over a wide temperature range (0~80 °C). In addition, the optimization results of experimental conditions showed that the optimal pH for the preparation of AgNPs was around 7.5, and the optimal range of AgNO3 concentration used was 1.76×10-4~3.33×10-4 mol/L. Finally, using AgNPs prepared by lemon juice reduction method for pesticide residue SERS detection on the surface of fruits and vegetables, the detection limits for paraquat and carbendazim in solution were as low as 10-14 and 10-10 M, respectively, and the concentration of pesticides showed a good linear relationship with Raman spectral intensity. The lowest detection limits for paraquat and carbendazim residues on different fruits and vegetables were as low as 3.90 ng/kg and 0.22 µg/kg, respectively. Conclusions This work provides a green and convenient method for preparing SERS materials for rapid detection of pesticide residues on fruits and vegetables. This method has practical value for universal operation. The prepared AgNPs can be used for trace pesticide residue detection, providing a pathway for rapid and sensitive detection of pesticide residues in agricultural products. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Method for Extracting Strawberry Leaf Age and Canopy Width Based on a Mobile Phenotyping Platform and Instance Segmentation Technology FAN Jiangchuan, WANG Yuanqiao, GOU Wenbo, CAI Shuangze, GUO Xinyu, ZHAO Chunjiang Smart Agriculture    DOI: 10.12133/j.smartag.SA202310014 Online available: 29 March 2024

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Price Game Model and Competitive Strategy of Agricultural Products Retail Market in the Context of Blockchain XUE Bing, SUN Chuanheng, LIU Shuangyin, LUO Na, LI Jinhui Smart Agriculture    DOI: 10.12133/j.smartag.SA202309027 Online available: 01 April 2024

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Zero-Shot Pest Identification Based on Generative Adversarial Networks and Visual-Semantic Alignment LI Tianjun, YANG Xinting, CHEN Xiao, HU Huan, ZHOU Zijie, LI Wenyong Smart Agriculture    DOI: 10.12133/j.smartag.SA202312014 Online available: 07 April 2024