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Agricultural Metaverse: Key Technologies, Application Scenarios, Challenges and Prospects CHEN Feng, SUN Chuanheng, XING Bin, LUO Na, LIU Haishen Smart Agriculture    2022, 4 (4): 126-137.   DOI: 10.12133/j.smartag.SA202206006 Abstract （1535）   HTML （244）    PDF（pc） （1045KB）（4104）       Knowledge map    Save As an emerging concept, metaverse has attracted extensive attention from industry, academia and scientific research field. The combination of agriculture and metaverse will greatly promote the development of agricultural informatization and agricultural intelligence, provide new impetus for the transformation and upgrading of agricultural intelligence. Firstly, to expound feasibility of the application research of metaverse in agriculture, the basic principle and key technologies of agriculture metaverse were briefly described, such as blockchain, non-fungible token, 5G/6G, artificial intelligence, Internet of Things, 3D reconstruction, cloud computing, edge computing, augmented reality, virtual reality, mixed reality, brain computer interface, digital twins and parallel system. Then, the main scenarios of three agricultural applications of metaverse in the fields of virtual farm, agricultural teaching system and agricultural product traceability system were discussed. Among them, virtual farm is one of the most important applications of agricultural metaverse. Agricultural metaverse can help the growth of crops and the raising of livestock and poultry in the field of agricultural production, provide a three-dimensional and visual virtual leisure agricultural experience, provide virtual characters in the field of agricultural product promotion. The agricultural metaverse teaching system can provide virtual agricultural teaching similar to natural scenes, save training time and improve training efficiency by means of fragmentation. Traceability of agricultural products can let consumers know the production information of agricultural products and feel more confident about enterprises and products. Finally, the challenges in the development of agricultural metaverse were summarized in the aspects of difficulties in establishing agricultural metaverse system, weak communication foundation of agricultural metaverse, immature agricultural metaverse hardware equipment and uncertain agricultural meta universe operation, and the future development directions of agricultural metaverse were prospected. In the future, researches on the application of metaverse, agricultural growth mechanism, and low power wireless communication technologies are suggested to be carried out. A rural broadband network covering households can be established. The industrialization application of agricultural meta universe can be promoted. This review can provide theoretical references and technical supports for the development of metaverse in the field of agriculture. Reference | Related Articles | Metrics | Comments（0）

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State-of-the-art and Prospect of Research on Key Technical for Unmanned Farms of Field Corp YIN Yanxin, MENG Zhijun, ZHAO Chunjiang, WANG Hao, WEN Changkai, CHEN Jingping, LI Liwei, DU Jingwei, WANG Pei, AN Xiaofei, SHANG Yehua, ZHANG Anqi, YAN Bingxin, WU Guangwei Smart Agriculture    2022, 4 (4): 1-25.   DOI: 10.12133/j.smartag.SA202212005 Abstract （2584）   HTML （635）    PDF（pc） （2582KB）（3515）       Knowledge map    Save As one of the important way for constructing smart agriculture, unmanned farms are the most attractive in nowadays, and have been explored in many countries. Generally, data, knowledge and intelligent equipment are the core elements of unmanned farms. It deeply integrates modern information technologies such as the Internet of Things, big data, cloud computing, edge computing, and artificial intelligence with agriculture to realize agricultural production information perception, quantitative decision-making, intelligent control, precise input and personalized services. In the paper, the overall technical architecture of unmanned farms is introduced, and five kinds of key technologies of unmanned farms are proposed, which include information perception and intelligent decision-making technology, precision control technology and key equipment for agriculture, automatic driving technology in agriculture, unmanned operation agricultural equipment, management and remote controlling system for unmanned farms. Furthermore, the latest research progress of the above technologies both worldwide are analyzed. Based on which, critical scientific and technological issues to be solved for developing unmanned farms in China are proposed, include unstructured environment perception of farmland, automatic drive for agriculture machinery in complex and changeable farmland environment, autonomous task assignment and path planning of unmanned agricultural machinery, autonomous cooperative operation control of unmanned agricultural machinery group. Those technologies are challenging and absolutely, and would be the most competitive commanding height in the future. The maize unmanned farm constructed in the city of Gongzhuling, Jilin province, China, was also introduced in detail. The unmanned farms is mainly composed of information perception system, unmanned agricultural equipment, management and controlling system. The perception system obtains and provides the farmland information, maize growth, pest and disease information of the farm. The unmanned agricultural machineries could complete the whole process of the maize mechanization under unattended conditions. The management and controlling system includes the basic GIS, remote controlling subsystem, precision operation management subsystem and working display system for unmanned agricultural machineries. The application of the maize unmanned farm has improved maize production efficiency (the harvesting efficiency has been increased by 3－4 times) and reduced labors. Finally, the paper summarizes the important role of the unmanned farm technology were summarized in solving the problems such as reduction of labors, analyzes the opportunities and challenges of developing unmanned farms in China, and put forward the strategic goals and ideas of developing unmanned farm in China. Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Enlightenment of Japanese Harvesting Robot in Facility Agriculture HUANG Zichen, SUGIYAMA Saki Smart Agriculture    2022, 4 (2): 135-149.   DOI: 10.12133/j.smartag.SA202202008 Abstract （1013）   HTML （153）    PDF（pc） （1780KB）（3268）       Knowledge map    Save Intelligent equipment is necessary to ensure stable, high-quality, and efficient production of facility agriculture. Among them, intelligent harvesting equipment needs to be designed and developed according to the characteristics of fruits and vegetables, so there is little large-scale mechanization. The intelligent harvesting equipment in Japan has nearly 40 years of research and development history since the 1980s, and the review of its research and development products has specific inspiration and reference significance. First, the preferential policies that can be used for harvesting robots in the support policies of the government and banks to promote the development of facility agriculture were introduced. Then, the development of agricultural robots in Japan was reviewed. The top ten fruits and vegetables in the greenhouse were selected, and the harvesting research of tomato, eggplant, green pepper, cucumber, melon, asparagus, and strawberry harvesting robots based on the combination of agricultural machinery and agronomy was analyzed. Next, the commercialized solutions for tomato, green pepper, and strawberry harvesting system were detailed and reviewed. Among them, taking the green pepper harvesting robot developed by the start-up company AGRIST Ltd. in recent years as an example, the harvesting robot developed by the company based on the Internet of Things technology and artificial intelligence algorithms was explained. This harvesting robot can work 24 h a day and can control the robot's operation through the network. Then, the typical strawberry harvesting robot that had undergone four generations of prototype development were reviewed. The fourth-generation system was a systematic solution developed by the company and researchers. It consisted of high-density movable seedbeds and a harvesting robot with the advantages of high space utilization, all-day work, and intelligent quality grading. The strengths, weaknesses, challenges, and future trends of prototype and industrialized solutions developed by universities were also summarized. Finally, suggestions for accelerating the development of intelligent, smart, and industrialized harvesting robots in China's facility agriculture were provided. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Infield Corn Kernel Detection and Counting Based on Multiple Deep Learning Networks LIU Xiaohang, ZHANG Zhao, LIU Jiaying, ZHANG Man, LI Han, FLORES Paulo, HAN Xiongzhe Smart Agriculture    2022, 4 (4): 49-60.   DOI: 10.12133/j.smartag.SA202207004 Abstract （1063）   HTML （115）    PDF（pc） （3336KB）（2130）       Knowledge map    Save Machine vision has been increasingly used for agricultural sensing tasks. The detection method based on deep learning for infield corn kernels can improve the detection accuracy. In order to obtain the number of lost corn kernels quickly and accurately after the corn harvest, and evaluate the corn harvest combine performance on grain loss, the method of directly using deep learning technology to count corn kernels in the field was developed and evaluated. Firstly, an RGB camera was used to collect image with different backgrounds and illuminations, and the datasets were generated. Secondly, different target detection networks for kernel recognition were constructed, including Mask R-CNN, EfficientDet-D5, YOLOv5-L and YOLOX-L, and the collected 420 effective images were used to train, verify and test each model. The number of images in train, verify and test datasets were 200, 40 and 180, respectively. Finally, the counting performances of different models were evaluated and compared according to the recognition results of test set images. The experimental results showed that among the four models, YOLOv5-L had overall the best performance, and could reliably identify corn kernels under different scenes and light conditions. The average precision (AP) value of the model for the image detection of the test set was 78.3%, and the size of the model was 89.3 MB. The correct rate of kernel count detection in four scenes of non-occlusion, surface mid-level-occlusion, surface severe-occlusion and aggregation were 98.2%, 95.5%, 76.1% and 83.3%, respectively, and F1 values were 94.7%, 93.8%, 82.8% and 87%, respectively. The overall detection correct rate and F1 value of the test set were 90.7% and 91.1%, respectively. The frame rate was 55.55 f/s, and the detection and counting performance were better than Mask R-CNN, EfficientDet-D5 and YOLOX-L networks. The detection accuracy was improved by about 5% compared with the second best performance of Mask R-CNN. With good precision, high throughput, and proven generalization, YOLOv5-L can realize real-time monitoring of corn harvest loss in practical operation. Reference | Related Articles | Metrics | Comments（0）

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Detection of Pear Inflorescence Based on Improved Ghost-YOLOv5s-BiFPN Algorithm XIA Ye, LEI Xiaohui, QI Yannan, XU Tao, YUAN Quanchun, PAN Jian, JIANG Saike, LYU Xiaolan Smart Agriculture    2022, 4 (3): 108-119.   DOI: 10.12133/j.smartag.SA202207006 Abstract （498）   HTML （68）    PDF（pc） （2214KB）（2054）       Knowledge map    Save Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning. Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior WANG Zheng, SONG Huaibo, WANG Yunfei, HUA Zhixin, LI Rong, XU Xingshi Smart Agriculture    2022, 4 (2): 36-52.   DOI: 10.12133/j.smartag.SA202203011 Abstract （859）   HTML （110）    PDF（pc） （1155KB）（2026）       Knowledge map    Save The motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of expounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the development status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representative livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable devices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy, and there is still a certain distance from commercial use. Finally, the future development directions of relevant key technologies were prospected, including miniaturization and integration of wearable monitoring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Intelligent Knowledge Service: Overview and Future Perspectives ZHAO Ruixue, YANG Chenxue, ZHENG Jianhua, LI Jiao, WANG Jian Smart Agriculture    2022, 4 (4): 105-125.   DOI: 10.12133/j.smartag.SA202207009 Abstract （977）   HTML （138）    PDF（pc） （1435KB）（1724）       Knowledge map    Save The wide application of advanced information technologies such as big data, Internet of Things and artificial intelligence in agriculture has promoted the modernization of agriculture in rural areas and the development of smart agriculture. This trend has also led to the boost of demands for technology and knowledge from a large amount of agricultural business entities. Faced with problems such as dispersiveness of knowledges, hysteric knowledge update, inadequate agricultural information service and prominent contradiction between supply and demand of knowledge, the agricultural knowledge service has become an important engine for the transformation, upgrading and high-quality development of agriculture. To better facilitate the agriculture modernization in China, the research and application perspectives of agricultural knowledge services were summarized and analyzed. According to the whole life cycle of agricultural data, based on the whole agricultural industry chain, a systematic framework for the construction of agricultural intelligent knowledge service systems towards the requirement of agricultural business entities was proposed. Three layers of techniques in necessity were designed, ranging from AIoT-based agricultural situation perception to big data aggregation and governance, and from agricultural knowledge organization to computation/mining based on knowledge graph and then to multi-scenario-based agricultural intelligent knowledge service. A wide range of key technologies with comprehensive discussion on their applications in agricultural intelligent knowledge service were summarized, including the aerial and ground integrated Artificial Intelligence & Internet-of-Things (AIoT) full-dimensional of agricultural condition perception, multi-source heterogeneous agricultural big data aggregation/governance, knowledge modeling, knowledge extraction, knowledge fusion, knowledge reasoning, cross-media retrieval, intelligent question answering, personalized recommendation, decision support. At the end, the future development trends and countermeasures were discussed, from the aspects of agricultural data acquisition, model construction, knowledge organization, intelligent knowledge service technology and application promotion. It can be concluded that the agricultural intelligent knowledge service is the key to resolve the contradiction between supply and demand of agricultural knowledge service, can provide support in the realization of the advance from agricultural cross-media data analytics to knowledge reasoning, and promote the upgrade of agricultural knowledge service to be more personalized, more precise and more intelligent. Agricultural knowledge service is also an important support for agricultural science and technologies to be more self-reliance, modernized, and facilitates substantial development and upgrading of them in a more effective manner. Reference | Related Articles | Metrics | Comments（0）

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Crop Stress Sensing and Plant Phenotyping Systems: A Review BAI Geng, GE Yufeng Smart Agriculture    2023, 5 (1): 66-81.   DOI: 10.12133/j.smartag.SA202211001 Abstract （377）   HTML （49）    PDF（pc） （1595KB）（1636）       Knowledge map    Save Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Key Technologies and Equipment for Smart Orchard Construction and Prospects HAN Leng, HE Xiongkui, WANG Changling, LIU Yajia, SONG Jianli, QI Peng, LIU Limin, LI Tian, ZHENG Yi, LIN Guihai, ZHOU Zhan, HUANG Kang, WANG Zhong, ZHA Hainie, ZHANG Guoshan, ZHOU Guotao, MA Yong, FU Hao, NIE Hongyuan, ZENG Aijun, ZHANG Wei Smart Agriculture    2022, 4 (3): 1-11.   DOI: 10.12133/j.smartag.SA200201014 Abstract （1535）   HTML （421）    PDF（pc） （2824KB）（1540）       Knowledge map    Save Traditional orchard production is facing problems of labor shortage due to the aging, difficulties in the management of agricultural equipment and production materials, and low production efficiency which can be expected to be solved by building a smart orchard that integrates technologies of Internet of Things(IoT), big data, equipment intelligence, et al. In this study, based on the objectives of full mechanization and intelligent management, a smart orchard was built in Pinggu district, an important peaches and pears fruit producing area of Beijing. The orchard covers an aera of more than 30 hm2 in Xiying village, Yukou town. In the orchard, more than 10 kinds of information acquisition sensors for pests, diseases, water, fertilizers and medicines are applied, 28 kinds of agricultural machineries with intelligent technical support are equipped. The key technologies used include: intelligent information acquisition system, integrated water and fertilizer management system and intelligent pest management system. The intelligent operation equipment system includes: unmanned lawn mower, intelligent anti-freeze machine, trenching and fertilizer machine, automatic driving crawler, intelligent profiling variable sprayer, six-rotor branch-to-target drone, multi-functional picking platform and finishing and pruning machine, etc. At the same time, an intelligent management platform has been built in the smart orchard. The comparison results showed that, smart orchard production can reduce labor costs by more than 50%, save pesticide dosage by 30% ~ 40%, fertilizer dosage by 25% ~ 35%, irrigation water consumption by 60% ~ 70%, and comprehensive economic benefits increased by 32.5%. The popularization and application of smart orchards will further promote China's fruit production level and facilitate the development of smart agriculture in China. Reference | Related Articles | Metrics | Comments（0）

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Goals, Key Technologies, and Regional Models of Smart Farming for Field Crops in China LI Li, LI Minzan, LIU Gang, ZHANG Man, WANG Maohua Smart Agriculture    2022, 4 (4): 26-34.   DOI: 10.12133/j.smartag.SA202207003 Abstract （1416）   HTML （202）    PDF（pc） （853KB）（1522）       Knowledge map    Save Smart farming for field crops is a significant part of the smart agriculture. It aims at crop production, integrating modern sensing technology, new generation mobile communication technology, computer and network technology, Internet of Things(IoT), big data, cloud computing, blockchain and expert wisdom and knowledge. Deeply integrated application of biotechnology, engineering technology, information technology and management technology, it realizes accurate perception, quantitative decision-making, intelligent operation and intelligent service in the process of crop production, to significantly improve land output, resource utilization and labor productivity, comprehensively improves the quality, and promotes efficiency of agricultural products. In order to promote the sustainable development of the smart farming, through the analysis of the development process of smart agriculture, the overall objectives and key tasks of the development strategy were clarified, the key technologies in smart farming were condensed. Analysis and breakthrough of smart farming key technologies were crucial to the industrial development strategy. The main problems of the smart farming for field crops include: the lack of in-situ accurate measurement technology and special agricultural sensors, the large difference between crop model and actual production, the instantaneity, reliability, universality, and stability of the information transmission technologies, and the combination of intelligent agricultural equipment with agronomy. Based on the above analysis, five primary technologies and eighteen corresponding secondary technologies of smart farming for field crops were proposed, including: sensing technologies of environmental and biological information in field, agricultural IoT technologies and mobile internet, cloud computing and cloud service technologies in agriculture, big data analysis and decision-making technology in agriculture, and intelligent agricultural machinery and agricultural robots in fireld production. According to the characteristics of China's cropping region, the corresponding smart farming development strategies were proposed: large-scale smart production development zone in the Northeast region and Inner Mongolia region, smart urban agriculture and water-saving agriculture development zone in the region of Beijing, Tianjin, Hebei and Shandong, large-scale smart farming of cotton and smart dry farming green development comprehensive test zone in the Northwest arid region, smart farming of rice comprehensive development test zone in the Southeast coast region, and characteristic smart farming development zone in the Southwest mountain region. Finally, the suggestions were given from the perspective of infrastructure, key technology, talent and policy. Reference | Related Articles | Metrics | Comments（0）

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Advances in the Applications of Deep Learning Technology for Livestock Smart Farming GUO Yangyang, DU Shuzeng, QIAO Yongliang, LIANG Dong Smart Agriculture    2023, 5 (1): 52-65.   DOI: 10.12133/j.smartag.SA202205009 Abstract （789）   HTML （135）    PDF（pc） （1118KB）（1439）       Knowledge map    Save Accurate and efficient monitoring of animal information, timely analysis of animal physiological and physical health conditions, and automatic feeding and farming management combined with intelligent technologies are of great significance for large-scale livestock farming. Deep learning techniques, with automatic feature extraction and powerful image representation capabilities, solve many visual challenges, and are more suitable for application in monitoring animal information in complex livestock farming environments. In order to further analyze the research and application of artificial intelligence technology in intelligent animal farming, this paper presents the current state of research on deep learning techniques for tag detection recognition, body condition evaluation and weight estimation, and behavior recognition and quantitative analysis for cattle, sheep and pigs. Among them, target detection and recognition is conducive to the construction of electronic archives of individual animals, on which basis the body condition and weight information, behavior information and health status of animals can be related, which is also the trend of intelligent animal farming. At present, intelligent animal farming still faces many problems and challenges, such as the existence of multiple perspectives, multi-scale, multiple scenarios and even small sample size of a certain behavior in data samples, which greatly increases the detection difficulty and the generalization of intelligent technology application. In addition, animal breeding and animal habits are a long-term process. How to accurately monitor the animal health information in real time and effectively feed it back to the producer is also a technical difficulty. According to the actual feeding and management needs of animal farming, the development of intelligent animal farming is prospected and put forward. First, enrich the samples and build a multi perspective dataset, and combine semi supervised or small sample learning methods to improve the generalization ability of in-depth learning models, so as to realize the perception and analysis of the animal's physical environment. Secondly, the unified cooperation and harmonious development of human, intelligent equipment and breeding animals will improve the breeding efficiency and management level as a whole. Third, the deep integration of big data, deep learning technology and animal farming will greatly promote the development of intelligent animal farming. Last, research on the interpretability and security of artificial intelligence technology represented by deep learning model in the breeding field. And other development suggestions to further promote intelligent animal farming. Aiming at the progress of research application of deep learning in livestock smart farming, it provides reference for the modernization and intelligent development of livestock farming. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Detection Method for Dragon Fruit in Natural Environment Based on Improved YOLOX SHANG Fengnan, ZHOU Xuecheng, LIANG Yingkai, XIAO Mingwei, CHEN Qiao, LUO Chendi Smart Agriculture    2022, 4 (3): 120-131.   DOI: 10.12133/j.smartag.SA202207001 Abstract （422）   HTML （52）    PDF（pc） （2267KB）（1197）       Knowledge map    Save Dragon fruit detection in natural environment is the prerequisite for fruit harvesting robots to perform harvesting. In order to improve the harvesting efficiency, by improving YOLOX (You Only Look Once X) network, a target detection network with an attention module was proposed in this research. As the benchmark, YOLOX-Nano network was chose to facilitate deployment on embedded devices, and the convolutional block attention module (CBAM) was added to the backbone feature extraction network of YOLOX-Nano, which improved the robustness of the model to dragon fruit target detection to a certain extent. The correlation of features between different channels was learned by weight allocation coefficients of features of different scales, which were extracted for the backbone network. Moreover, the transmission of deep information of network structure was strengthened, which aimed at reducing the interference of dragon fruit recognition in the natural environment as well as improving the accuracy and speed of detection significantly. The performance evaluation and comparison test of the method were carried out. The results showed that, after training, the dragon fruit target detection network got an AP0.5 value of 98.9% in the test set, an AP0.5:0.95 value of 72.4% and F1 score was 0.99. Compared with other YOLO network models under the same experimental conditions, on the one hand, the improved YOLOX-Nano network model proposed in this research was more lightweight, on the other hand, the detection accuracy of this method surpassed that of YOLOv3, YOLOv4 and YOLOv5 respectively. The average detection accuracy of the improved YOLOX-Nano target detection network was the highest, reaching 98.9%, 26.2% higher than YOLOv3, 9.8% points higher than YOLOv4-Tiny, and 7.9% points higher than YOLOv5-S. Finally, real-time tests were performed on videos with different input resolutions. The improved YOLOX-Nano target detection network proposed in this research had an average detection time of 21.72 ms for a single image. In terms of the size of the network model was only 3.76 MB, which was convenient for deployment on embedded devices. In conclusion, not only did the improved YOLOX-Nano target detection network model accurately detect dragon fruit under different lighting and occlusion conditions, but the detection speed and detection accuracy showed in this research could able to meet the requirements of dragon fruit harvesting in natural environment requirements at the same time, which could provide some guidance for the design of the dragon fruit harvesting robot. Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Outlook of Livestock Feeding Robot YANG Liang, XIONG Benhai, WANG Hui, CHEN Ruipeng, ZHAO Yiguang Smart Agriculture    2022, 4 (2): 86-98.   DOI: 10.12133/j.smartag.SA202204001 Abstract （817）   HTML （94）    PDF（pc） （1912KB）（1184）       Knowledge map    Save The production mode of livestock breeding has changed from extensive to intensive, and the production level is improved. However, low labor productivity and labor shortage have seriously restricted the rapid development of China's livestock breeding industry. As a new intelligent agricultural machinery equipment, agricultural robot integrates advanced technologies, such as intelligent monitoring, automatic control, image recognition technology, environmental modeling algorithm, sensors, flexible execution, etc. Using modern information and artificial intelligence technology, developing livestock feeding and pushing robots, realizing digital and intelligent livestock breeding, improving livestock breeding productivity are the main ways to solve the above contradiction. In order to deeply analyze the research status of robot technology in livestock breeding, products and literature were collected worldwide. This paper mainly introduced the research progress of livestock feeding robot from three aspects: Rail feeding robot, self-propelled feeding robot and pushing robot, and analyzed the technical characteristics and practical application of feeding robot.The rail feeding robot runs automatically along the fixed track, identifies the target animal, positions itself, and accurately completes feed delivery through preset programs to achieve accurate feeding of livestock. The self-propelled feeding robot can walk freely in the farm and has automatic navigation and positioning functions. The system takes single chip microcomputer as the control core and realizes automatic walking by sensor and communication module. Compared with the rail feeding robot, the feeding process is more flexible, convenient and technical, which is more conducive to the promotion and application of livestock farms. The pushing robot will automatically push the feed to the feeding area, promote the increase of feed intake of livestock, and effectively reduce the labor demand of the farm. By comparing the feeding robots of developed countries and China from two aspects of technology and application, it is found that China has achieved some innovation in technology, while developted countries do better in product application. The development of livestock robot was prospected. In terms of strategic planning, it is necessary to keep up with the international situation and the trend of technological development, and formulate the agricultural robot development strategic planning in line with China's national conditions. In terms of the development of core technologies, more attention should be paid to the integration of information perception, intelligent sensors and deep learning algorithms to realize human-computer interaction. In terms of future development trends, it is urgent to strengthen innovation, improve the friendliness and intelligence of the robot, and improve the learning ability of the robot. To sum up, intelligent livestock feeding and pushing machine operation has become a cutting-edge technology in the field of intelligent agriculture, which will surely lead to a new round of agricultural production technology reform, promote the transformation and upgrading of China's livestock industry. . Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Advances and Challenges in Physiological Parameters Monitoring and Diseases Diagnosing of Dairy Cows Based on Computer Vision KANG Xi, LIU Gang, CHU Mengyuan, LI Qian, WANG Yanchao Smart Agriculture    2022, 4 (2): 1-18.   DOI: 10.12133/j.smartag.SA202204005 Abstract （1093）   HTML （204）    PDF（pc） （1097KB）（1178）       Knowledge map    Save Realizing the construction of intelligent farming by using advanced information technology, thus improving the living welfare of dairy cows and the economic benefits of dairy farms has become an important goal and task in dairy farming research field. Computer vision technology has the advantages of non-contact, stress-free, low cost and high throughput, and has a broad application prospect in animal production. On the basis of describing the importance of computer vision technology in the development of intelligent farming industry, this paper introduced the cutting-edge technology of cow physiological parameters and disease diagnosis based on computer vision, including cow temperature monitoring, body size monitoring, weight measurement, mastitis detection and lameness detection. The introduction coverd the development process of these studies, the current mainstream techniques, and discussed the problems and challenges in the research and application of related technology, aiming at the problem that the current computer vision-based detection methods are susceptible to individual difference and environmental changes. Combined with the development status of farming industry, suggestions on how to improve the universality of computer vision technology in intelligent farming industry, how to improve the accuracy of monitoring cows' physiological parameters and disease diagnosis, and how to reduce the influence of environment on the system were put forward. Future research work should focus on research and developmentof algorithm, make full use of computer vision technology continuous detection and the advantage of large amount of data, to ensure the accuracy of the detection, and improve the function of the system integration and data utilization, expand the computer vision system function. Under the premise that does not affect the ability of the system, to improve the study on the number of function integration and system function and reduce equipment costs. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Multi-Class on-Tree Peach Detection Using Improved YOLOv5s and Multi-Modal Images LUO Qing, RAO Yuan, JIN Xiu, JIANG Zhaohui, WANG Tan, WANG Fengyi, ZHANG Wu Smart Agriculture    2022, 4 (4): 84-104.   DOI: 10.12133/j.smartag.SA202210004 Abstract （889）   HTML （104）    PDF（pc） （3285KB）（1143）       Knowledge map    Save Accurate peach detection is a prerequisite for automated agronomic management, e.g., peach mechanical harvesting. However, due to uneven illumination and ubiquitous occlusion, it is challenging to detect the peaches, especially when the peaches are bagged in orchards. To this end, an accurate multi-class peach detection method was proposed by means of improving YOLOv5s and using multi-modal visual data for mechanical harvesting in this paper. RGB-D dataset with multi-class annotations of naked and bagging peach was proposed, including 4127 multi-modal images of corresponding pixel-aligned color, depth, and infrared images acquired with consumer-level RGB-D camera. Subsequently, an improved lightweight YOLOv5s (small depth) model was put forward by introducing a direction-aware and position-sensitive attention mechanism, which could capture long-range dependencies along one spatial direction and preserve precise positional information along the other spatial direction, helping the networks accurately detect peach targets. Meanwhile, the depthwise separable convolution was employed to reduce the model computation by decomposing the convolution operation into convolution in the depth direction and convolution in the width and height directions, which helped to speed up the training and inference of the network while maintaining accuracy. The comparison experimental results demonstrated that the improved YOLOv5s using multi-modal visual data recorded the detection mAP of 98.6% and 88.9% on the naked and bagging peach with 5.05 M model parameters in complex illumination and severe occlusion environment, increasing by 5.3% and 16.5% than only using RGB images, as well as by 2.8% and 6.2% when compared to YOLOv5s. As compared with other networks in detecting bagging peaches, the improved YOLOv5s performed best in terms of mAP, which was 16.3%, 8.1% and 4.5% higher than YOLOX-Nano, PP-YOLO-Tiny, and EfficientDet-D0, respectively. In addition, the proposed improved YOLOv5s model offered better results in different degrees than other methods in detecting Fuji apple and Hayward kiwifruit, verified the effectiveness on different fruit detection tasks. Further investigation revealed the contribution of each imaging modality, as well as the proposed improvement in YOLOv5s, to favorable detection results of both naked and bagging peaches in natural orchards. Additionally, on the popular mobile hardware platform, it was found out that the improved YOLOv5s model could implement 19 times detection per second with the considered five-channel multi-modal images, offering real-time peach detection. These promising results demonstrated the potential of the improved YOLOv5s and multi-modal visual data with multi-class annotations to achieve visual intelligence of automated fruit harvesting systems. Reference | Related Articles | Metrics | Comments（0）

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Agricultural Knowledge Intelligent Service Technology: A Review ZHAO Chunjiang Smart Agriculture    2023, 5 (2): 126-148.   DOI: 10.12133/j.smartag.SA202306002 Abstract （870）   HTML （279）    PDF（pc） （3579KB）（1136）       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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Advances in Forage Crop Growth Monitoring by UAV Remote Sensing ZHUO Yue, DING Feng, YAN Haijun, XU Jing Smart Agriculture    2022, 4 (4): 35-48.   DOI: 10.12133/j.smartag.SA202206004 Abstract （836）   HTML （117）    PDF（pc） （863KB）（1133）       Knowledge map    Save Dynamic monitoring and quantitative estimation of forage crop growth are of great importance to the large-scale production of forage crop. UAV remote sensing has the advantages of high resolution, strong flexibility and low cost. In recent years, it has developed rapidly in the field of forage crop growth monitoring. In order to clarify the development status of forage crop growth monitoring and find the development direction, first, methods of UAV crop remote sensing monitoring were briefly described from two aspects of data acquisition and processing. Second, three key technologies of forage crop including canopy information extraction, spectral feature optimization and forage biomass estimation were described. Then the development trend of related research in recent years was analyzed, and it was pointed out that the number of papers published on UAV remote sensing forage crop monitoring showed an overall trend of rapidly increasing. With the rapid development of computer information technology and remote sensing technology, the application potential of UAV in the field of forage crop monitoring has been fully explored. Then, the research progress of UAV remote sensing in forage crop growth monitoring was described in five parts according to sensor types, i.e., visible, multispectral, hyperspectral, thermal infrared and LiDAR, and the research of each type of sensor were summarized and reviewed, pointing out that the current researches of hyperspectral, thermal infrared and LiDAR sensors in forage crop monitoring were less than that of visible and multispectral sensors. Finally, the future development directions were clarified according to the key technical problems that have not been solved in the research and application of UAV remote sensing forage crop growth monitoring: (1) Build a multi-temporal growth monitoring model based on the characteristics of different growth stages and different growth years of forage crops, carry out UAV remote sensing monitoring of forage crops around representative research areas to further improve the scope of application of the model. (2) Establish a multi-source database of UAV remote sensing, and carry out integrated collaborative monitoring combined with satellite remote sensing data, historical yield, soil conductivity and other data. (3) Develop an intelligent and user-friendly UAV remote sensing data analysis system, and shorten the data processing time through 5G communication network and edge computing devices. This paper could provide relevant technical references and directional guidelines for researchers in the field of forage crops and further promote the application and development of precision agriculture technology. Reference | Related Articles | Metrics | Comments（0）

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Automatic Spraying Technology and Facilities for Pipeline Spraying in Mountainous Orchards SONG Shuran, HU Shengyang, SUN Daozong, DAI Qiufang, XUE Xiuyun, XIE Jiaxing, LI Zhen Smart Agriculture    2022, 4 (3): 86-94.   DOI: 10.12133/j.smartag.SA202205005 Abstract （339）   HTML （32）    PDF（pc） （1218KB）（1128）       Knowledge map    Save The orchard in the mountainous area is rugged and steep, and there is no road for large-scale plant protection machinery traveling in the orchard, so it is difficult for mobile spraying machinery to enter. In order to solve the above problems, the automatic pipeline spraying technology and facilities were studied. A pipeline automatic spraying facility suitable for mountainous orchards was designed, which included spraying head, field spraying pipeline, automatic spraying controller and spraying groups. The spraying head was composed of a spraying unit and a constant pressure control system, which pressurized the pesticide liquid and stabilized the liquid pressure according to the preset pressure value to ensure a better atomization effect. Field spraying pipeline consisted of main pipeline, valves and spraying groups. In order to perform automatic spraying, a solenoid valve was installed between the main pipeline and each spraying group, and the automatic spraying operation of each spraying group was controlled automatically by the opening or closing of the solenoid valve. An automatic spraying controller composed of main controller, solenoid valve driving circuit, solenoid valve controlling node and power supplying unit was developed, and the controlling software was also programmed in this research. The main controller had manual and automatic two working modes. The solenoid valve controlling node was used to send wireless signals to the main controller and receive wireless signals from the main controller, and open or close the corresponding solenoid valve according to the received control signal. During the spraying operation, the pesticide liquid flowed into the orchard from the spray head through the pipeline. The automatic spray controller was used to control the solenoid valve to open or close the spray group one by one, and implement manual control or automatic control of spraying. In order to determine the continuous opening time of the solenoid valve, an effectiveness of the spray test was carried out. The spraying test results showed that spraying effectiveness could be guaranteed by opening solenoid valve for 8 s continuously. The efficiency of this pipeline automatic spraying facility was 2.61 hm2/h, which was 45－150 times that of manual spraying, and 2.1 times that of unmanned aerial vehicle spraying. The automatic pipeline spraying technology in mountainous orchards had obvious advantages in the timeliness of pest controlling. This research can provide references and ideas for the development of spray technology and intelligent spraying facilities in mountainous orchards. Reference | Related Articles | Metrics | Comments（0）

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Machine Learning Inversion Model of Soil Salinity in the Yellow River Delta Based on Field Hyperspectral and UAV Multispectral Data FAN Chengzhi, WANG Ziwen, YANG Xingchao, LUO Yongkai, XU Xuexin, GUO Bin, LI Zhenhai Smart Agriculture    2022, 4 (4): 61-73.   DOI: 10.12133/j.smartag.SA202212001 Abstract （694）   HTML （47）    PDF（pc） （1831KB）（1121）       Knowledge map    Save Soil salinization in the Yellow River Delta is a difficult and miscellaneous disease to restrict the development of agricultural economy, and further hinders agricultural production. To explore the retrieval of soil salt content from remote sensing images under the condition of no vegetation coverage, the typical area of the Yellow River Delta was taken as the study area to obtain the hyperspectral of surface features, the multispectral of UAVs and the soil salt content of sample points. Three representative experimental areas with flat terrain and obvious soil salinization characteristics were set up in the study area, and 90 samples were collected in total. By optimizing the sensitive spectral parameters, machine learning algorithms of partial least squares regression (PLSR) and random forest (RF) for inversion of soil salt content were used in the study area. The results showed that: (1) Hyperspectral band of 1972 nm had the highest sensitivity to soil salt content, with correlation r of -0.31. The optimized spectral parameters of shortwave infrared can improve the accuracy of estimating soil salt content. (2) RF model optimized by two different data sources had better stability than PLSR model. RF model performed well in terms of generalization ability and balance error, but it had some over-fitting problems. (3) RF model based on ground feature hyperspectral (R2 =0.54, verified RMSE=3.30 g/kg) was superior to the random forest model based on UAV multispectral (R2 =0.54, verified RMSE=3.35 g/kg). The combination of image texture features improved the estimation accuracy of multispectral model, but the verification accuracy was still lower than that of hyperspectral model. (4) Soil salt content based on UAV multi-spectral imageries and RF model was mapped in the study area. This study demonstrates that the level of soil salinization in the Yellow River Delta region is significantly different in geographical location. The cultivated land in the study area is mainly light and moderate salinized soil with has certain restrictions on crop cultivation. Areas with low soil salt content are suitable for planting crops in low salinity fields, and farmland with high soil salt content is suitable for planting crops with high salinity tolerance. This study constructed and compared the soil salinity inversion models of the Yellow River Delta from two different sources of data, optimized them based on the advantages of each data source, explored the inversion of soil salinity content without vegetation coverage, and can provide a reference for more accurate inversion of land salinization. Reference | Related Articles | Metrics | Comments（0）

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Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning ZHUANG Jiayu, XU Shiwei, LI Yang, XIONG Lu, LIU Kebao, ZHONG Zhiping Smart Agriculture    2022, 4 (2): 174-182.   DOI: 10.12133/j.smartag.SA202203013 Abstract （673）   HTML （74）    PDF（pc） （1057KB）（1067）       Knowledge map    Save To further improve the simulation and estimation accuracy of the supply and demand process of agricultural products, a large number of agricultural data at the national and provincial levels since 1980 were used as the basic research sample, including production, planted area, food consumption, industrial consumption, feed consumption, seed consumption, import, export, price, GDP, population, urban population, rural population, weather and so on, by fully considering the impact factors of agricultural products such as varieties, time, income and economic development, a multi-agricultural products supply and demand forecasting model based on long short-term memory neural network (LSTM) was constructed in this study. The general thought of supply and demand forecasting model is packaging deep neural network training model as an I/O-opening modular model, reserving control interface for input of outside data, and realizing the indicators forecasting of supply and demand and matrixing of balance sheet. The input of model included forecasting balance sheet data of agricultural products, annual price data, general economic data, and international currency data since 2000. The output of model was balance sheet data of next decade since forecasting time. Under the premise of fully considering the mechanical constraints, the model used the advantages of deep learning algorithms in nonlinear model analysis and prediction to analyze and predict supply and demand of 9 main types of agricultural products, including rice, wheat, corn, soybean, pork, poultry, beef, mutton, and aquatic products. The production forecast results of 2019－2021 based on this model were compared and verified with the data published by the National Bureau of Statistics, and the mean absolute percentage error was 3.02%, which meant the average forecast accuracy rate of 2019－2021 was 96.98%. The average forecast accuracy rate was 96.10% in 2019, 98.26% in 2020, and 96.58% in 2021, which shows that with the increase of sample size, the prediction effect of intelligent learning model would gradually get better. The forecasting results indicate that the multi-agricultural supply and demand prediction model based on LSTM constructed in this study can effectively reflect the impact of changes in hidden indicators on the prediction results, avoiding the uncontrollable error introduced by manual experience intervention. The model can provide data production and technical support such as market warning, policy evaluation, resource management and public opinion analysis for agricultural production and management and macroeconomic regulation, and can provide intelligent technical support for multi-regional and inter-temporal agricultural outlook work by monitoring agricultural operation data in a timely manner. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Typical Raman Spectroscopy Ttechnology and Research Progress in Agriculture Detection GAO Zhen, ZHAO Chunjiang, YANG Guiyan, DONG Daming Smart Agriculture    2022, 4 (2): 121-134.   DOI: 10.12133/j.smartag.SA202201013 Abstract （866）   HTML （82）    PDF（pc） （819KB）（1047）       Knowledge map    Save Raman spectroscopy is a type of scattering spectroscopy with features such as rapid, less susceptible to moisture interference, no sample pre-treatment and in vivo detection. As a powerful characterization tool for analyzing and testing the molecular composition and structure of substances, Raman spectroscopy is also playing an extremely important role in the detection of plant and animal phenotypes, food safety, soil and water quality in the agricultural field with the continuous improvement of Raman spectroscopy technology. In this paper, the detection principles of Raman spectroscopy are introduced, and the new progresses of eight Raman spectroscopy technology are summarized, including confocal microscopy Raman spectroscopy, Fourier transform Raman spectroscopy, surface-enhanced Raman spectroscopy, tip-enhanced Raman spectroscopy, resonance Raman spectroscopy, spatially shifted Raman spectroscopy, frequency-shifted excitation Raman difference spectroscopy and Raman spectroscopy based on nonlinear optics, etc. And their advantages and disadvantages and application scenarios are prerented, respectively. The applications of Raman spectroscopy in plant detection, soil detection, water quality detection, food detection, etc. are summarized. It can be specifically subdivided into plant phenotype, plant stress, soil pesticide residue detection, soil colony detection, soil nutrient detection, food pesticide detection, food quality detection, food adulteration detection, and water quality detection. In future agricultural applications, the elimination of fluorescence background due to complex living organisms in Raman spectroscopy is the next research direction. The study of stable enhanced substrates is an important direction in the application of Surface Enhanced Raman Spectroscopy (SERS). In order to meet the measurement of different scenarios, portable and telemetric Raman spectrometers will also play an important role in the future. Raman spectroscopy needs to be further explored for a wide variety of research objects in agriculture, especially for applications in animal science, for which there is still a paucity of relevant studies up to now. In the existing field of agricultural research, it is necessary to pursue the characterization of more specific substances by Raman spectroscopy, which can prompt the application of Raman spectroscopy for a wider range of uses in agriculture. Further, the pursuit of lower detection limits and higher stability for practical applications is also the direction of development of Raman spectroscopy in the field of agriculture. Finally, the challenges that need to be solved and the future development directions of Raman spectroscopy are proposed in the field of agriculture in order to bring more inspiration to future agricultural production and research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Pig Sound Analysis: A Measure of Welfare JI Nan, YIN Yanling, SHEN Weizheng, KOU Shengli, DAI Baisheng, WANG Guowei Smart Agriculture    2022, 4 (2): 19-35.   DOI: 10.12133/j.smartag.SA202204004 Abstract （694）   HTML （59）    PDF（pc） （700KB）（987）       Knowledge map    Save Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Identifying Multiple Apple Leaf Diseases Based on the Improved CBAM-ResNet18 Model Under Weak Supervision ZHANG Wenjing, JIANG Zezhong, QIN Lifeng Smart Agriculture    2023, 5 (1): 111-121.   DOI: 10.12133/j.smartag.SA202301005 Abstract （629）   HTML （85）    PDF（pc） （1490KB）（961）       Knowledge map    Save To deal with the issues of low accuracy of apple leaf disease images recognition under weak supervision with only image category labeling, an improved CBAM-ResNet-based algorithm was proposed in this research. Using ResNet18 as the base model, the multilayer perceptron (MLP) in the lightweight convolutional block attention module (CBAM) attention mechanism channel was improved by up-dimensioning to amplify the details of apple leaf disease features. The improved CBAM attention module was incorporated into the residual module to enhance the key details of AlphaDropout with SeLU (Scaled Exponential Linearunits) to prevent overfitting of its network and accelerate the convergence effect of the model. Finally, the learning rate was adjusted using a single-cycle cosine annealing algorithm to obtain the disease recognition model. The training test was performed under weak supervision with only image-level annotation of all sample images, which greatly reduced the annotation cost. Through ablation experiments, the best dimensional improvement of MLP in CBAM was explored as 2. Compared with the original CBAM, the accuracy rate was increased by 0.32%, and the training time of each round was reduced by 8 s when the number of parameters increased by 17.59%. Tests were conducted on a dataset of 6185 images containing five diseases, including apple spotted leaf drop, brown spot, mosaic, gray spot, and rust, and the results showed that the model achieved an average recognition accuracy of 98.44% for the five apple diseases under weakly supervised learning. The improved CBAM-ResNet18 had increased by 1.47% compared with the pre-improved ResNet18, and was higher than VGG16, DesNet121, ResNet50, ResNeXt50, EfficientNet-B0 and Xception control model. In terms of learning efficiency, the improved CBAM-ResNet18 compared to ResNet18 reduced the training time of each round by 6 s under the condition that the number of parameters increased by 24.9%, and completed model training at the fastest speed of 137 s per round in VGG16, DesNet121, ResNet50, ResNeXt50, Efficient Net-B0 and Xception control models. Through the results of the confusion matrix, the average precision, average recall rate, and average F1 score of the model were calculated to reach 98.43%, 98.46%, and 0.9845, respectively. The results showed that the proposed improved CBAM-ResNet18 model could perform apple leaf disease identification and had good identification results, and could provide technical support for intelligent apple leaf disease identification providing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Automatic Measurement of Multi-Posture Beef Cattle Body Size Based on Depth Image YE Wenshuai, KANG Xi, HE Zhijiang, LI Mengfei, LIU Gang Smart Agriculture    2022, 4 (4): 144-155.   DOI: 10.12133/j.smartag.SA202210001 Abstract （898）   HTML （69）    PDF（pc） （1532KB）（951）       Knowledge map    Save Beef cattle in the farm are active, which leads the collection of posture of the beef cattle changeable, so it is difficult to automatically measure the body size of the beef cattle. Aiming at the above problems, an automatic measurement method for beef cattle's body size under multi-pose was proposed by analyzing the skeleton features of beef cattle head and the edge contour features of beef cattle images. Firstly, the consumer-grade depth camera Azure Kinect DK was used to collect the top-view depth video data directly above the beef cattle and the video data were divided into frames to obtain the original depth image. Secondly, the original depth image was processed by shadow interpolation, normalization, image segmentation and connected domain to remove the complex background and obtain the target image containing only beef cattle. Thirdly, the Zhang-Suen algorithm was used to extract the beef cattle skeleton of the target image, and calculated the intersection points and endpoints of the skeleton, so as to analyze the characteristics of the beef cattle head to determine the head removal point, and to remove the beef cattle head information from the image. Finally, the curvature curve of the beef cattle profile was obtained by the improved U-chord curvature method. The body measurement points were determined according to the curvature value and converted into three-dimensional spaces to calculate the body size parameters. In this paper, the postures of beef cattle, which were analyzed by a large amount of depth image data, were divided into left crooked, right crooked, correct posture, head down and head up, respectively. The test results showed that the head removal method proposed based on the skeleton in multiple postures hads head removel success rate higher than 92% in the five postures. Using the body measurement point extraction method based on the improved U-chord curvature proposed, the average absolute error of body length measurement was 2.73 cm, the average absolute error of body height measurement was 2.07 cm, and the average absolute error of belly width measurement was 1.47 cm. The method provides a better way to achieve the automatic measurement of beef cattle body size in multiple poses. Reference | Related Articles | Metrics | Comments（0）

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Three-Dimensional Virtual Orchard Construction Method Based on Laser Point Cloud FENG Han, ZHANG Hao, WANG Zi, JIANG Shijie, LIU Weihong, ZHOU Linghui, WANG Yaxiong, KANG Feng, LIU Xingxing, ZHENG Yongjun Smart Agriculture    2022, 4 (3): 12-23.   DOI: 10.12133/j.smartag.SA202207002 Abstract （666）   HTML （85）    PDF（pc） （2426KB）（945）       Knowledge map    Save To solve the problems of low level of digitalization of orchard management and relatively single construction method, a three-dimensional virtual orchard construction method based on laser point cloud was proposed in this research. First, the hand-held 3D point cloud acquistion equipment (3D-BOX) combined with the lidar odometry and mapping (SLAM-LOAM) algorithm was used to complete the acquisition of the point cloud data set of orchard; then the outliers and noise points of the point cloud data were removed by using the statistical filtering algorithm, which was based on the K-neighbor distance statistical method. To achieve this, a distance threshold model for removing noise points was established. When a discrete point exceeded, it would be marked as an outlier, and the point was separated from the point cloud dataset to achieve the effect of discrete point filtering. The VoxelGrid filter was used for down sampling, the cloth simulation filtering (CSF) cloth simulation algorithm was used to calculate the distance between the cloth grid points and the corresponding laser point cloud, and the distinction between ground points and non-ground points was achieved by dividing the distance threshold, and when combined with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, ground removal and cluster segmentation of orchard were realized; finally, the Unity3D engine was used to build a virtual orchard roaming scene, and convert the real-time GPS data of the operating equipment from the WGS-84 coordinate system to the Gauss projection plane coordinate system through Gaussian projection forward calculation. The real-time trajectory of the equipment was displayed through the LineRenderer, which realized the visual display of the motion trajectory control and operation trajectory of the working machine. In order to verify the effectiveness of the virtual orchard construction method, the test of orchard construction method was carried out in the Begonia fruit and the mango orchard. The results showed that the proposed point cloud data processing method could achieve the accuracy of cluster segmentation of Begonia fruit trees and mango trees 95.3% and 98.2%, respectively. Compared with the row spacing and plant spacing of fruit trees in the actual mango orchard, the average inter-row error of the virtual mango orchard was about 3.5%, and the average inter-plant error was about 6.6%. And compared the virtual orchard constructed by Unity3D with the actual orchard, the proposed method can effectively reproduce the actual three-dimensional situation of the orchard, and obtain a better visualization effect, which provides a technical solution for the digital modeling and management of the orchard. Reference | Related Articles | Metrics | Comments（0）

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Review on Energy Efficiency Assessment and Carbon Emission Accounting of Food Cold Chain WANG Xiang, ZOU Jingui, LI You, SUN Yun, ZHANG Xiaoshuan Smart Agriculture    2023, 5 (1): 1-21.   DOI: 10.12133/j.smartag.SA202301007 Abstract （502）   HTML （154）    PDF（pc） （1296KB）（943）       Knowledge map    Save The global energy is increasingly tight, and the global temperature is gradually rising. Energy efficiency assessment and carbon emission accounting can provide theoretical tools and practical support for the formulation of energy conservation and emission reduction strategies for the food cold chain, and is also a prerequisite for the sustainable development of the food cold chain. In this paper, the relationship and differences between energy consumption and carbon emissions in the general food cold chain are first described, and the principle, advantages and disadvantages of three energy consumption conversion standards of solar emergy value, standard coal and equivalent electricity are discussed. Besides, the possibilities of applying these three energy consumption conversion standards to energy consumption analysis and energy efficiency evaluation of food cold chain are explored. Then, for a batch of fresh agricultural products, the energy consumption of six links of the food cold chain, including the first transportation, the manufacturer, the second transportation, the distribution center, the third transportation, and the retailer, are systematically and comprehensively analyzed from the product level, and the comprehensive energy consumption level of the food cold chain are obtained. On this basis, ten energy efficiency indicators from five aspects of macro energy efficiency are proposed, including micro energy efficiency, energy economy, environmental energy efficiency and comprehensive energy efficiency, and constructs the energy efficiency evaluation index system of food cold chain. At the same time, other energy efficiency evaluation indicators and methods are also summarized. In addition, the standard of carbon emission conversion of food cold chain, namely carbon dioxide equivalent is introduce, the boundary of carbon emission accounting is determined, and the carbon emission factors of China's electricity is mainly discussed. Furthermore, the origin, principle, advantages and disadvantages of the emission factor method, the life cycle assessment method, the input-output analysis method and the hybrid life cycle assessment method, and the basic process of life cycle assessment method in the calculation of food cold chain carbon footprint are also reviewed. In order to improve the energy efficiency level of the food cold chain and reduce the carbon emissions of each link of the food cold chain, energy conservation and emission reduction methods for food cold chain are proposed from five aspects: refrigerant, distribution path, energy, phase change cool storage technology and digital twin technology. Finally, the energy efficiency assessment and carbon emission accounting of the food cold chain are briefly prospected in order to provide reference for promoting the sustainable development of China's food cold chain. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （58）   HTML （34）    PDF（pc） （1671KB）（876）       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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Autonomous Navigation and Automatic Target Spraying Robot for Orchards LIU Limin, HE Xiongkui, LIU Weihong, LIU Ziyan, HAN Hu, LI Yangfan Smart Agriculture    2022, 4 (3): 63-74.   DOI: 10.12133/j.smartag.SA202207008 Abstract （529）   HTML （49）    PDF（pc） （1905KB）（864）       Knowledge map    Save To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02°. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss. Reference | Related Articles | Metrics | Comments（0）

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Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example ZUO Min, HU Tianyu, DONG Wei, ZHANG Kexin, ZHANG Qingchuan Smart Agriculture    2023, 5 (1): 34-43.   DOI: 10.12133/j.smartag.SA202302001 Abstract （407）   HTML （146）    PDF（pc） （1323KB）（862）       Knowledge map    Save Ensuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability. However, the forecasting of agricultural products logistics demand is actually complicated, and it will be affected by various factors in the forecasting process. Therefore, in order to ensure the accuracy of forecasting the logistics demand of agricultural products, many influencing factors need to be considered. In this study, the logistics demand of agricultural products is taken as the research object, relevant indicators from 2017 to 2021 were selected as characteristic independent variables and a neural network model for forecasting the logistics demand of agricultural products was constructed by using Informer neural network. Taking Henan province, Hubei province and Hunan province in Central China as examples, the logistics demands of agricultural products in the three provinces were predicted. At the same time, long short-term memory network (LSTM) and Transformer neural network were used to forecast the demand of agricultural products logistics in three provinces of Central China, and the prediction results of the three models were compared. The results showed that the average percentage of prediction test error based on Informer neural network model constructed in this study was 3.39%, which was lower than that of LSTM and Transformer neural network models of 4.43% and 4.35%. The predicted value of Informer neural network model for three provinces was close to the actual value. The predicted value of Henan province in 2021 was 4185.33, the actual value was 4048.10, and the error was 3.389%. The predicted value of Hubei province in 2021 was 2503.64, the actual value was 2421.78, and the error was 3.380%. The predicted value of Hunan province in 2021 was 2933.31, the actual value was 2836.86, and the error was 3.340%. Therefore, it showed that the model can accurately predict the demand of agricultural products logistics in three provinces of Central China, and can provide a basis for rational planning and policy making of agricultural products logistics. Finally, the model and parameters were used to predict the logistics demand of agricultural products in Henan, Hunan, and Hubei provinces in 2023, and the predicted value of Henan province in 2023 was 4217.13; Hubei province was 2521.47, and Hunan province was 2974.65, respectively. The predicted values for the three provinces in 2023 are higher than the predicted values in 2021. Therefore, based on the logistics and transportation supporting facilities in 2021, it is necessary to ensure logistics and transportation efficiency and strengthen logistics and transportation capacity, so as to meet the growing logistics demand in Central China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Application of Artificial Intelligence in Agricultural Risk Management: A Review GUI Zechun, ZHAO Sijian Smart Agriculture    2023, 5 (1): 82-98.   DOI: 10.12133/j.smartag.SA202211004 Abstract （585）   HTML （85）    PDF（pc） （1410KB）（849）       Knowledge map    Save Agriculture is a basic industry deeply related to the national economy and people's livelihood, while it is also a weak industry. There are some problems with traditional agricultural risk management research methods, such as insufficient mining of nonlinear information, low accuracy and poor robustness. Artificial intelligence(AI) has powerful functions such as strong nonlinear fitting, end-to-end modeling, feature self-learning based on big data, which can solve the above problems well. The research progress of artificial intelligence technology in agricultural vulnerability assessment, agricultural risk prediction and agricultural damage assessment were first analyzed in this paper, and the following conclusions were obtained: 1. The feature importance assessment of AI in agricultural vulnerability assessment lacks scientific and effective verification indicators, and the application method makes it impossible to compare the advantages and disadvantages of multiple AI models. Therefore, it is suggested to use subjective and objective methods for evaluation; 2. In risk prediction, it is found that with the increase of prediction time, the prediction ability of machine learning model tends to decline. Overfitting is a common problem in risk prediction, and there are few researches on the mining of spatial information of graph data; 3. Complex agricultural production environment and varied application scenarios are important factors affecting the accuracy of damage assessment. Improving the feature extraction ability and robustness of deep learning models is a key and difficult issue to be overcome in future technological development. Then, in view of the performance improvement problem and small sample problem existing in the application process of AI technology, corresponding solutions were put forward. For the performance improvement problem, according to the user's familiarity with artificial intelligence, a variety of model comparison method, model group method and neural network structure optimization method can be used respectively to improve the performance of the model; For the problem of small samples, data augmentation, GAN (Generative Adversarial Network) and transfer learning can often be combined to increase the amount of input data of the model, enhance the robustness of the model, accelerate the training speed of the model and improve the accuracy of model recognition. Finally, the applications of AI in agricultural risk management were prospected: In the future, AI algorithm could be considered in the construction of agricultural vulnerability curve; In view of the relationship between upstream and downstream of agricultural industry chain and agriculture-related industries, the graph neural network can be used more in the future to further study the agricultural price risk prediction; In the modeling process of future damage assessment, more professional knowledge related to the assessment target can be introduced to enhance the feature learning of the target, and expanding the small sample data is also the key subject of future research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress of Apple Production Intelligent Chassis and Weeding and Harvesting Equipment Technology DUAN Luojia, YANG Fuzeng, YAN Bin, SHI shuaiqi, QIN jifeng Smart Agriculture    2022, 4 (3): 24-41.   DOI: 10.12133/j.smartag.SA202206010 Abstract （233）   HTML （28）    PDF（pc） （1521KB）（842）       Knowledge map    Save As a pillar industry of economic development in the main apple-producing areas, apple industry has made important contributions to the increase of local farmers' income. With the transformation and upgrading of apple industry, the mechanization and intelligence level would be directly related to economic benefits. To promote the research of apple production intelligent technology and the development of intelligent equipment, in this paper, the current level of mechanization in each step of apple production was first introduced. Then, the main characteristics of the main apple orchard machinery, such as power chassis, weeding machinery, and harvesting equipment, were demonstrated. The application progress of automatic leveling and control, automatic navigation, automatic obstacle avoidance, weed identification, weed removal, apple identification, apple positioning, apple separation, and other technologies in intelligent power chassis, intelligent weeding machines, and apple harvesting robots, were summarized. The basic principles and characteristics of the above three key technologies of intelligent equipment were expounded in combination with different application environments. Intelligent control is the key technology for the intelligentization of orchard power chassis. The post of chassis adaptive control technology and autonomous navigation technology were discussed. In addition, a chassis intelligent perception and intelligent decision-making system should be established. Orchard chassis safe, accurate, efficient, and stable driving and operation is the future development trend of orchard intelligent chassis. The lack of robust weed sensing technology is the main limitation to the commercial development of a robotic weed control system. To improve the level of weed detection and weeding, machine vision and multi-sensor fusion methods have been proposed to solve the practical problems, such as illumination, overlapping leaves, occlusion, and classifier or network structure optimization. Robotic apple harvesting has proven to be a highly challenging task due to environmental complexities, sensor reliability, and robot stability. To improve the accuracy and efficiency of harvest mechanization applications in apples, apple quick identification under complex scenes, apple picking path planning, and materials and structure of manipulator for apple picking must all be optimized accordingly. Finally, the challenges of intelligent equipment technologies in apple production were analyzed, and the developing suggestions were put forward. This research can provide references and ideas for the advancement of intelligent technology research in apple production and the research and development of intelligent equipment. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Technological Revolution, Disruptive Technology and Smart Agriculture HU Ruifa, LIU Wanjiawen Smart Agriculture    2022, 4 (4): 138-143.   DOI: 10.12133/j.smartag.SA202205002 Abstract （1000）   HTML （135）    PDF（pc） （475KB）（809）       Knowledge map    Save This paper described the concept and basic satisfaction of scientific and technological revolution, defines the endogenous and exogenous agricultural disruptive technology. The revolution of agricultural science and technology refers to the process in which the key disruptive core technology innovation applied to agricultural production drives a series of technological innovations adopted in production. Endogenous disruptive technology in agriculture refers to technology indicators that can fundamentally change the original technology, such as productivity improvement, overturn the economic or social necessity of the adoption of the original technology, and completely replace the original technology. Particularly the paper puts forwards the concept of the transboundary technology and demonstrates its endogenous application and impacts on the development of agricultural industry. The transboundary technology for exogenous application refers to the technology whose original invention and innovation are applied in non-agricultural fields and has nothing to do with agricultural industry. Focusing on smart agriculture and the typical transboundary technology, the paper analyzed the characteristics of the smart agriculture, discussed its impacts on the traditional agricultural production and rural transformation. Smart agriculture technology will be the disruptive core technology to promote a new round of technological and industrial revolution and rural transformation. It will fundamentally change the production mode of traditional agriculture, realize factory production and promote the revolutionary transformation of rural areas. The production and application of smart agricultural technology in China has shown good economic and social benefits and great potential for production and application. However, the application of intelligent agricultural technology based on artificial intelligence technology is still in the exploratory stage. As an agricultural application of transboundary technology, the smart agricultural technology with intelligent sensing technology as the core is not dominated by agricultural scientists like agricultural machinery technology revolution, chemical technology revolution and green revolution technology. At present, the application smart agriculture technology in China is only in its infancy. Hence, policy recommendations of strengthening key disruptive technology development, reforming agricultural higher education system, promoting the agricultural industry development of the transboundary technology, and pushing the application of smart agriculture technology to be implemented in high standard farmland and large-scale farms of agricultural production, etc., were proposed to promote the development of smart agriculture. Reference | Related Articles | Metrics | Comments（0）

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Real-Time Monitoring System for Rabbit House Environment Based on NB-IoT Network QIN Yingdong, JIA Wenshen Smart Agriculture    2023, 5 (1): 155-165.   DOI: 10.12133/j.smartag.SA202211008 Abstract （380）   HTML （50）    PDF（pc） （1662KB）（808）       Knowledge map    Save To meet the needs of environmental monitoring and regulation in rabbit houses, a real-time environmental monitoring system for rabbit houses was proposed based on narrow band Internet of Things (NB-IoT). The system overcomes the limitations of traditional wired networks, reduces network costs, circuit components, and expenses is low. An Arduino development board and the Quectel BC260Y-NB-IoT network module were used, along with the message queuing telemetry transport (MQTT) protocol for remote telemetry transmission, which enables network connectivity and communication with an IoT cloud platform. Multiple sensors, including SGP30, MQ137, and 5516 photoresistors, were integrated into the system to achieve real-time monitoring of various environmental parameters within the rabbit house, such as sound decibels, light intensity, humidity, temperature, and gas concentrations. The collected data was stored for further analysis and could be used to inform environmental regulation and monitoring in rabbit houses, both locally and in the cloud. Signal alerts based on circuit principles were triggered when thresholds were exceeded, creating an optimal living environment for the rabbits. The advantages of NB-IoT networks and other networks, such as Wi-Fi and LoRa were compared. The technology and process of building a system based on the three-layer architecture of the Internet of Things was introduced. The prices of circuit components were analyzed, and the total cost of the entire system was less than 400 RMB. The system underwent network and energy consumption tests, and the transmission stability, reliability, and energy consumption were reasonable and consistent across different time periods, locations, and network connection methods. An average of 0.57 transactions per second (TPS) was processed by the NB-IoT network using the MQTT communication protocol, and 34.2 messages per minute were sent and received with a fluctuation of 1 message. The monitored device was found to have an average voltage of approximately 12.5 V, a current of approximately 0.42 A, and an average power of 5.3 W after continuous monitoring using an electricity meter. No additional power consumption was observed during communication. The performance of various sensors was tested through a 24-hour indoor test, during which temperature and lighting conditions showed different variations corresponding to day and night cycles. The readings were stably and accurately captured by the environmental sensors, demonstrating their suitability for long-term monitoring purposes. This system is can provide equipment cost and network selection reference values for remote or large-scale livestock monitoring devices. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Identification and Counting of Silkworms in Factory Farm Using Improved Mask R-CNN Model HE Ruimin, ZHENG Kefeng, WEI Qinyang, ZHANG Xiaobin, ZHANG Jun, ZHU Yihang, ZHAO Yiying, GU Qing Smart Agriculture    2022, 4 (2): 163-173.   DOI: 10.12133/j.smartag.SA202201012 Abstract （412）   HTML （30）    PDF（pc） （2357KB）（803）       Knowledge map    Save Factory-like rearing of silkworm (Bombyx mori) using artificial diet for all instars is a brand-new rearing mode of silkworm. Accurate feeding is one of the core technologies to save cost and increase efficiency in factory silkworm rearing. Automatic identification and counting of silkworm play a key role to realize accurate feeding. In this study, a machine vision system was used to obtain digital images of silkworms during main instars, and an improved Mask R-CNN model was proposed to detect the silkworms and residual artificial diet. The original Mask R-CNN was improved using the noise data of annotations by adding a pixel reweighting strategy and a bounding box fine-tuning strategy to the model frame. A more robust model was trained to improve the detection and segmentation abilities of silkworm and residual feed. Three different data augmentation methods were used to expand the training dataset. The influences of silkworm instars, data augmentation, and the overlap between silkworms on the model performance were evaluated. Then the improved Mask R-CNN was used to detect silkworms and residual feed. The AP50 (Average Precision at IoU=0.5) of the model for silkworm detection and segmentation were 0.790 and 0.795, respectively, and the detection accuracy was 96.83%. The detection and segmentation AP50 of residual feed were 0.641 and 0.653, respectively, and the detection accuracy was 87.71%. The model was deployed on the NVIDIA Jetson AGX Xavier development board with an average detection time of 1.32 s and a maximum detection time of 2.05 s for a image. The computational speed of the improved Mask R-CNN can meet the requirement of real-time detection of the moving unit of the silkworm box on the production line. The model trained by the fifth instar data showed a better performance on test data than the fourth instar model. The brightness enhancement method had the greatest contribution to the model performance as compared to the other data augmentation methods. The overlap between silkworms also negatively affected the performance of the model. This study can provide a core algorithm for the research and development of the accurate feeding information system and feeding device for factory silkworm rearing, which can improve the utilization rate of artificial diet and improve the production and management level of factory silkworm rearing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Corn and Soybean Futures Price Intelligent Forecasting Based on Deep Learning XU Yulin, KANG Mengzhen, WANG Xiujuan, HUA Jing, WANG Haoyu, SHEN Zhen Smart Agriculture    2022, 4 (4): 156-163.   DOI: 10.12133/j.smartag.SA20220712 Abstract （817）   HTML （99）    PDF（pc） （872KB）（790）       Knowledge map    Save Corn and soybean are upland grain in the same season, and the contradiction of scrambling for land between corn and soybean is prominent in China, so it is necessary to explore the price relations between corn and soybean. In addition, agricultural futures have the function of price discovery compared with the spot. Therefore, the analysis and prediction of corn and soybean futures prices are of great significance for the management department to adjust the planting structure and for farmers to select the crop varieties. In this study, the correlation between corn and soybean futures prices was analyzed, and it was found that the corn and soybean futures prices have a strong correlation by correlation test, and soybean futures price is the Granger reason of corn futures price by Granger causality test. Then, the corn and soybean futures prices were predicted using a long short-term memory (LSTM) model. To optimize the futures price prediction model performance, Attention mechanism was introduced as Attention-LSTM to assign weights to the outputs of the LSTM model at different times. Specifically, LSTM model was used to process the input sequence of futures prices, the Attention layer assign different weights to the outputs, and then the model output the prediction results after a layer of linearity. The experimental results showed that Attention-LSTM model could significantly improve the prediction performance of both corn and soybean futures prices compared to autoregressive integrated moving average model (ARIMA), support vector regression model (SVR), and LSTM. For example, mean absolute error (MAE) was improved by 3.8% and 3.3%, root mean square error (RMSE) was improved by 0.6% and 1.8% and mean absolute error percentage (MAPE) was improved by 4.8% and 2.9% compared with a single LSTM, respectively. Finally, the corn futures prices were forecasted using historical corn and soybean futures prices together. Specifically, two LSTM models were used to process the input sequences of corn futures prices and soybean futures prices respectively, two parameters were trained to perform a weighted summation of the output of two LSTM models, and the prediction results were output by the model after a layer of linearity. The experimental results showed that MAE was improved by 6.9%, RMSE was improved by 1.1% and MAPE was improved by 5.3% compared with the LSTM model using only corn futures prices. The results verify the strong correlation between corn and soybean futures prices at the same time. In conclusion, the results verify the Attention-LSTM model can improve the performances of soybean and corn futures price forecasting compared with the general prediction model, and the combination of related agricultural futures price data can improve the prediction performances of agricultural product futures forecasting model. Reference | Related Articles | Metrics | Comments（0）

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High Quality Ramie Resource Screening Based on UAV Remote Sensing Phenotype Monitoring FU Hongyu, WANG Wei, LIAO Ao, YUE Yunkai, XU Mingzhi, WANG Ziwei, CHEN Jianfu, SHE Wei, CUI Guoxian Smart Agriculture    2022, 4 (4): 74-83.   DOI: 10.12133/j.smartag.SA202209001 Abstract （731）   HTML （38）    PDF（pc） （1187KB）（770）       Knowledge map    Save Ramie is an important fiber crop. Due to the shortage of land resources and the promotion of excellent varieties, the genetic variation and diversity of ramie decreased, which increased the need for investigation and protection of the ramie germplasm resources diversity. The crop phenotype measurement method based on UAV remote sensing can conduct frequent, rapid, non-destructive and accurate monitoring of different genotypes, which can fulfill the investigation of crop germplasm resources and screen specific and high-quality varieties. In order to realize efficient comprehensive evaluation of ramie germplasm phenotype and assist in screening of dominant ramie varieties, a method for monitoring and screening ramie germplasm phenotype was proposed based on UAV remote sensing images. Firstly, based on UAV remote sensing images, the digital surface model (DSM) and orthophoto of the test area were generated by Pix4dmapper. Then, the key phenotypic parameters (plant height, plant number, leaf area index, leaf chlorophyll content and water content) of ramie germplasm resources were estimated. The subtraction method was used to extract ramie plant height based on DSM, while the target detection algorithm was applied to extract ramie plant number based on orthographic images, and four machine learning methods were used to estimate the leaf area index (LAI), leaf chlorophyll content (SPAD value) and water content. Finally, according to the extracted remote sensing phenotypic parameters, the genetic diversity of ramie germplasm was analyzed by using variability analysis and principal component analysis. The results showed that: (1) The ramie phenotype estimation based on UAV remote sensing was effective, with the fitting accuracy of plant height 0.93, and the root mean square error (RMSE) 5.654 cm. The fitting indexes of SPAD value, water content and LAI were 0.66, 0.79 and 0.74, respectively, and RMSE were 2.03, 2.21 and 0.63, respectively; (2) The remote sensing phenotypes of ramie germplasm were significantly different, as the coefficients of variation of LAI, plant height and plant number reached 20.82%, 24.61% and 35.48%, respectively; (3) Principal component analysis was used to cluster the remote sensing phenotypes into factor 1 (plant height and LAI) and factor 2 (LAI and SPAD value), factor 1 can be used to evaluate the structural characteristics of ramie germplasm resources, and factor 2 can be used as the screening index of high-light efficiency ramie resources. This study could provide references for crop germplasm phenotypic monitoring and breeding correlation analysis. Reference | Related Articles | Metrics | Comments（0）

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Design and Key Technologies of Big Data Platform for Commercial Beef Cattle Breeding MA Weihong, LI Jiawei, WANG Zhiquan, GAO Ronghua, DING Luyu, YU Qinyang, YU Ligen, LAI Chengrong, LI Qifeng Smart Agriculture    2022, 4 (2): 99-109.   DOI: 10.12133/j.smartag.SA202203005 Abstract （478）   HTML （58）    PDF（pc） （1993KB）（749）       Knowledge map    Save Focusing on the low level of management and informatization and intelligence of the beef cattle industry in China, a big data platform for commercial beef cattle breeding, referring to the experience of international advanced beef cattle breeding countries, was proposed in this research. The functions of the platform includes integrating germplasm resources of beef cattle, automatic collecting of key beef cattle breeding traits, full-service support for the beef cattle breeding process, formation of big data analysis and decision-making system for beef cattle germplasm resources, and joint breeding innovation model. Aiming at the backward storage and sharing methods of beef cattle breeding data and incomplete information records in China, an information resource integration platform and an information database for beef cattle germplasm were established. Due to the vagueness and subjectivity of the breeding performance evaluation standard, a scientific online evaluation technology of beef cattle breeding traits and a non-contact automatic acquisition and intelligent calculation method were proposed. Considering the lack of scientific and systematic breeding planning and guidance for farmers in China, a full-service support system for beef cattle breeding and nanny-style breeding guidance during beef cattle breeding was developed. And an interactive progressive decision-making method for beef cattle breeding to solve the lack of data accumulation of beef cattle germplasm was proposed. The main body of breeding and farming enterprises was not closely integrated, according to that, the innovative breeding model of regional integration was explored. The idea of commercial beef cattle breeding big data software platform and the technological and model innovation content were also introduced. The technology innovations included the deep mining of germplasm resources data and improved breed management pedigree, the automatic acquisition and evaluation technology of non-contact breeding traits, the fusion of multi-source heterogeneous information to provide intelligent decision support. The future goal is to form a sustainable information solution for China's beef cattle breeding industry and improve the overall level of China's beef cattle breeding industry. 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 （368）   HTML （95）    PDF（pc） （2822KB）（696）       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 （414）   HTML （114）    PDF（pc） （1576KB）（696）       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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Automatic Acquisition and Target Extraction of Beef Cattle 3D Point Cloud from Complex Environment LI Jiawei, MA Weihong, LI Qifeng, XUE Xianglong, WANG Zhiquan Smart Agriculture    2022, 4 (2): 64-76.   DOI: 10.12133/j.smartag.SA202206003 Abstract （574）   HTML （51）    PDF（pc） （2809KB）（695）       Knowledge map    Save Non-contact measurement based on the point cloud acquisition technology is able to alleviate the stress responses among beef cattle while collecting core body dimension data, but the current 3D data collection for beef cattle is usually time-consuming and easily influenced by the environment, which is in fact inapplicable to the actual breeding environment. In order to overcome the difficulty in obtaining the complete beef cattle point clouds, a non-contact phenotype data acquisition equipment was developed with a 3D reconstruction function, which can provide a large amount of standardized 3D quantitative phenotype data for beef cattle breeding and fattening process. The system is made up of a Kinect DK depth camera, an infrared grating trigger, and an Radio Frequency Identification (RFID) trigger, which enables the multi-angle instantaneous acquisition of beef cattle point clouds when the beef cattle pass through the walkway. The point cloud processing algorithm was developed based on the C++ platform and Point Cloud Library (PCL), and 3D reconstruction of beef cattle point clouds was achieved through spatial and outlier point filtering, Random Sample Consensus (RANSAC) shape fitting, point cloud thinning, and perceptual box filtering based on the dimensionality reduction density clustering to effectively filter out the interference, such as noises from the railings close to the beef cattle, without destroying the integrity of the point clouds. In the present work, a total of 124 sets of point clouds were successfully collected from 20 beef cattles on the actual farm using this system, and the target extraction experiments were completed. Notably, the beef cattle passed through the walkway in a natural state without any intervention during the whole data collection process. The experimental results showed that the acquisition success rate of this device was 91.89%. The coordinate system of the collected point cloud was consistent with the real situation and the body dimension reconstruction error was 0.6%. This device can realize the automatic acquisition and 3D reconstruction of beef cattle point cloud data from multiple angles without human intervention, and can automatically extract the target beef cattle point clouds from a complex environment. The point cloud data collected by this system help to restore the body size and shape of beef cattle, thereby provide solid support for the measurement of core parameters such as body height, body width, body oblique length, chest circumference, abdominal circumference, and body weight. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Phenotypic Traits Extraction of Wheat Plants Using 3D Digitization ZHENG Chenxi, WEN Weiliang, LU Xianju, GUO Xinyu, ZHAO Chunjiang Smart Agriculture    2022, 4 (2): 150-162.   DOI: 10.12133/j.smartag.SA202203009 Abstract （567）   HTML （89）    PDF（pc） （1803KB）（676）       Knowledge map    Save Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic analysis of wheat plants, three-dimensional (3D) digitization was used to extract phenotypic parameters of wheat plants. Firstly, digital representation method of wheat organs was given and a 3D digital data acquisition standard suitable for the whole growth period of wheat was formulated. According to this standard, data acquisition was carried out using a 3D digitizer. Based on the definition of phenotypic parameters and semantic coordinates information contained in the 3D digitizing data, eleven conventional measurable phenotypic parameters in three categories were quantitative extracted, including lengths, thicknesses, and angles of wheat plants and organs. Furthermore, two types of new parameters for shoot architecture and 3D leaf shape were defined. Plant girth was defined to quantitatively describe the looseness or compactness by fitting 3D discrete coordinates based on the least square method. For leaf shape, wheat leaf curling and twisting were defined and quantified according to the direction change of leaf surface normal vector. Three wheat cultivars including FK13, XN979, and JM44 at three stages (rising stage, jointing stage, and heading stage) were used for method validation. The Open3D library was used to process and visualize wheat plant data. Visualization results showed that the acquired 3D digitization data of maize plants were realistic, and the data acquisition approach was capable to present morphological differences among different cultivars and growth stages. Validation results showed that the errors of stem length, leaf length, stem thickness, stem and leaf angle were relatively small. The R2 were 0.93, 0.98, 0.93, and 0.85, respectively. The error of the leaf width and leaf inclination angle were also satisfactory, the R2 were 0.75 and 0.73. Because wheat leaves are narrow and easy to curl, and some of the leaves have a large degree of bending, the error of leaf width and leaf angle were relatively larger than other parameters. The data acquisition procedure was rather time-consuming, while the data processing was quite efficient. It took around 133 ms to extract all mentioned parameters for a wheat plant containing 7 tillers and total 27 leaves. The proposed method could achieve convenient and accurate extraction of wheat phenotypes at individual plant and organ levels, and provide technical support for wheat shoot architecture related research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Status Quo of Waterfowl Intelligent Farming Research Review and Development Trend Analysis LIU Youfu, XIAO Deqin, ZHOU Jiaxin, BIAN Zhiyi, ZHAO Shengqiu, HUANG Yigui, WANG Wence Smart Agriculture    2023, 5 (1): 99-110.   DOI: 10.12133/j.smartag.SA202205007 Abstract （414）   HTML （39）    PDF（pc） （2057KB）（675）       Knowledge map    Save Waterfowl farming in China is developing rapidly in the direction of large-scale, standardization and intelligence. The research and application of intelligent farming equipment and information technology is the key to promote the healthy and sustainable development of waterfowl farming, which is important to improve the output efficiency of waterfowl farming, reduce the reliance on labor in the production process, fit the development concept of green and environmental protection and achieve high-quality transformational development. In this paper, the latest research and inventions of intelligent waterfowl equipment, waterfowl shed environment intelligent control technology and intelligent waterfowl feeding, drinking water, dosing and disinfection and automatic manure treatment equipment were introduced. At present, compared to pigs, chickens and cattle, the intelligent equipment of waterfowl are still relatively backward. Most waterfowl houses are equipped with chicken equipment directly, lacking improvements for waterfowl. Moreover, the linkage between the equipment is poor and not integrated with the breeding mode and shed structure of waterfowl, resulting in low utilization. Therefore, there is a need to develop and improve equipment for the physiological growth characteristics of waterfowl from the perspective of their breeding welfare. In addition, the latest research advances in the application of real-time production information collection and intelligent management technologies were present. The information collection technologies included visual imaging technology, sound capture systems, and wearable sensors were present. Since the researches of ducks and geese is few, the research of poultry field, which can provide a reference for the waterfowl were also summarized. The research of information perception and processing of waterfowl is currently in its initial stage. Information collection techniques need to be further tailored to the physiological growth characteristics of waterfowl, and better deep learning models need to be established. The waterfowl management platform, taking the intelligent management platform developed by South China Agricultural University as an example were also described. Finally, the intelligent application of the waterfowl industry was pointed out, and the future trends of intelligent farming with the development of mechanized and intelligent equipment for waterfowl in China to improve the recommendations were analyzed. The current waterfowl farming is in urgent need of intelligent equipment reform and upgrading of the industry for support. In the future, intelligent equipment for waterfowl, information perception methods and control platforms are in urgent to be developed. When upgrading the industry, it is necessary to develop a development strategy that fits the current waterfowl farming model in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （238）   HTML （47）    PDF（pc） （1659KB）（656）       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 （477）   HTML （143）    PDF（pc） （2277KB）（613）       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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Development of China Feed Nutrition Big Data Analysis Platform XIONG Benhai, ZHAO Yiguang, LUO Qingyao, ZHENG Shanshan, GAO Huajie Smart Agriculture    2022, 4 (2): 110-120.   DOI: 10.12133/j.smartag.SA202205003 Abstract （538）   HTML （41）    PDF（pc） （1590KB）（601）       Knowledge map    Save The shortage of feed grain is continually worsening in China, which leads to the transformation of feed grain security into national food security. Therefore, comprehensively integrating the basic data resources of feed nutrition and improving the nutritional value of all available feed resources will be one of the key technical strategies to ensure national food security in China. In this study, based on the description specification and attribute data standard of 16 categories of Chinese feed raw materials, more than 500,000 pieces of data on the types, spatial distribution, chemical composition and nutritional value characteristics of existing feed resources, which were accumulated through previous projects from the sixth Five-Year Plan to the thirteenth Five-Year Plan period, were digitally collected, recorded, categorized and comprehensively analyzed. By using MySQL relational database technology and PHP program, a new generation of feed nutrition big data online platform (http://www.chinafeeddata.org.cn/) was developed and web data sharing service was provided as well. First of all, the online platform provided visual analysis of all warehousing data, which could realize the visual comparison of a single or multiple feed nutrients in various graphic forms such as scatter diagram, histogram, curve line and column chart. By using two-dimensional code technology, all feed nutrition attribute data and feed entity sample traceability data could be shared and downloaded remotely in real-time on mobile phones. Secondly, the online platform also incorporated various regression models for prediction of feed effective nutrient values using readily available feed chemical composition in the datasets, providing dynamic analysis for feed raw material nutrient variation. Finally, based on Geographic Information System technology, the online platform integrated the data of feed chemical composition and major mineral element concentrations with their geographical location information, which was able to provide the distribution query and comparative analysis of the geographic information map of the feed raw material nutrition data at both provincial and national level. Meanwhile, the online platform can also provide a download service of the various datasets, which brought convenience to the comprehensive application of existing feed nutrition data. This research also showed that expanding feed resource data and providing prediction and analysis models of feed effective nutrients could maximize the utilization of the existing feed nutrition data. After embedding online calculation modules of various feed formulation software, this platform would be able to provide a one-stop service and optimize the utilization of the feed nutrition data. 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 （208）   HTML （44）    PDF（pc） （1858KB）（578）       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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Multiscale Feature Fusion Yak Face Recognition Algorithm Based on Transfer Learning CHEN Zhanqi, ZHANG Yu'an, WANG Wenzhi, LI Dan, HE Jie, SONG Rende Smart Agriculture    2022, 4 (2): 77-85.   DOI: 10.12133/j.smartag.SA202201001 Abstract （551）   HTML （74）    PDF（pc） （1841KB）（576）       Knowledge map    Save Identifying of yak is indispensable for individual documentation, behavior monitoring, precise feeding, disease prevention and control, food traceability, and individualized breeding. Aiming at the application requirements of animal individual identification technology in intelligent informatization animal breeding platforms, a yak face recognition algorithm based on transfer learning and multiscale feature fusion, i.e., transfer learning-multiscale feature fusion-VGG(T-M-VGG) was proposed. The sample data set of yak facial images was produced by a camera named GoPro HERO8 BLACK. Then, a part of dataset was increased by the data enhancement ways that involved rotating, adjusting the brightness and adding noise to improve the robustness and accuracy of model. T-M-VGG, a kind of convolutional neural network based on pre-trained visual geometry group network and transfer learning was input with normalized dataset samples. The feature map of Block3, Block4 and Block5 were considered as F3, F4 and F5, respectively. What's more, F3 and F5 were taken by the structure that composed of three parallel dilated convolutions, the dilation rate were one, two and three, respectively, to dilate the receptive filed which was the map size of feature map. Further, the multiscale feature maps were fused by the improved feature pyramid which was in the shape of stacked hourglass structure. Finally, the fully connected layer was replaced by the global average pooling to classify and reduce a large number of parameters. To verify the effectiveness of the proposed model, a comparative experiment was conducted. The experimental results showed that recognition accuracy rate in 38,800 data sets of 194 yaks reached 96.01%, but the storage size was 70.75 MB. Twelve images representing different yak categories from dataset were chosen randomly for occlusion test. The origin images were masked with different shape of occlusions. The accuracy of identifying yak individuals was 83.33% in the occlusion test, which showed that the model had mainly learned facial features. The proposed algorithm could provide a reference for research of yak face recognition and would be the foundation for the establishment of smart management platform. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Comparative Study of the Regulation Effects of Artificial Intelligence-Assisted Planting Strategies on Strawberry Production in Greenhouse GENG Wenxuan, ZHAO Junye, RUAN Jiwei, HOU Yuehui Smart Agriculture    2022, 4 (2): 183-193.   DOI: 10.12133/j.smartag.SA202203006 Abstract （475）   HTML （81）    PDF（pc） （869KB）（564）       Knowledge map    Save Artificial intelligence (AI) assisted planting can improve in the precise management of protected horticultural crops while also alleviating the increasingly prevalent problem of labor shortage. As a typical representative of labor-intensive industries, the strawberry industry has a growing need for intelligent technology. To assess the regulatory effects of various AI strategies and key technologies on strawberry production in greenhouse, as well as provide valuable references for the innovation and industrial application of AI in horticultural crops, four AI planting strategies were evaluated. Four 96 m2 modern greenhouses were used for planting strawberry plants. Each greenhouse was equipped with standard sensors and actuators, and growers used artificial intelligence algorithms to remotely control the greenhouse climate and crop growth. The regulatory effects of four different AI planting strategies on strawberry growth, fruit yield and qualitywere compared and analyzed. And human-operated cultivation was taken as a reference to analyze the characteristics, existing problems and shortages. Each AI planting strategy simulated and forecast the greenhouse environment and crop growth by constructing models. AI-1 implemented greenhouse management decisions primarily through the knowledge graph method, whereas AI-2 transferred the intelligent planting model of Dutch greenhouse tomato planting to strawberry planting. AI-3 and AI-4 created growth and development models for strawberries based on World Food Studies (WOFOST) and Product of Thermal Effectiveness and Photosynthesis Active Radiation (TEP), respectively. The results showed that all AI supported strategy outperformed a human-operated greenhouse that served as reference. In comparison to the human-operated cultivation group, the average yield and output value of the AI planting strategy group increased 1.66 and 1.82 times, respectively, while the highest Return on Investment increased 1.27 times. AI can effectively improve the accuracy of strawberry planting management and regulation, reduce water, fertilizer, labor input, and obtain higher returns under greenhouse production conditions equipped with relatively complete intelligent equipment and control components, all with the goal of high yield and quality. Key technologies such as knowledge graphs, deep learning, visual recognition, crop models, and crop growth simulators all played a unique role in strawberry AI planting. The average yield and Return on Investment (ROI) of the AI groups were greater than those of the human-operated cultivation group. More specifically, the regulation of AI-1 on crop development and production was relatively stable, integrating expert experience, crop data, and environmental data with knowledge graphs to create a standardized strawberry planting knowledge structure as well as intelligent planting decision-making approach. In this study, AI-1 achieved the highest yield, the heaviest average fruit weight, and the highest ROI. This group's AI-assisted strategy optimized the regulatory effect of growth, development, and yield formation of strawberry crops in consideration of high yield and quality. However, there are still issues to be resolved, such as the difficulty of simulating the disturbance caused by manual management and collecting crop ontology data. 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 （265）   HTML （51）    PDF（pc） （1686KB）（545）       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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Accurate Extraction of Apple Orchard on the Loess Plateau Based on Improved Linknet Network ZHANG Zhibo, ZHAO Xining, GAO Xiaodong, ZHANG Li, YANG Menghao Smart Agriculture    2022, 4 (3): 95-107.   DOI: 10.12133/j.smartag.SA202206001 Abstract （324）   HTML （18）    PDF（pc） （2040KB）（531）       Knowledge map    Save The rapid increasing of apple planting area on the Loess Plateau has exerted an important influence on the regional eco-hydrology and socio-economic development. However, the orchards in this area are small and complex, and there are only county or city scale statistical data, lack of actual spatial distribution information. To this end, for the extraction of apple orchards on the Loess Plateau, in this study, a professional dataset of low-altitude remote sensing images acquired by unmanned aerial vehicle was firstly established. The R_34_Linknet network and other five commonly used deep learning semantic segmentation models SegNet, FCN_8s, DeeplabV3+, UNet and Linknet were applied to the spatial distribution extraction of apple orchards on the Loess Plateau, and the best-performing model was R_34_Linknet, with a F1 score of 87.1%, a pixel accuracy (PA) of 92.3%, an mean intersection over union (MioU) of 81.2%, a frequency weighted intersection over union (FWIoU) of 85.7%, and the mean pixel accuracy (MPA) was 89.6%. The spatial pyramid pool structure (ASPP) and R_34_Linknet network was combined to expand the receptive field of the network and get R_34_Linknet_ASPP network, and then ASPP structure was improved. Combining the spatial pyramid pooling (ASPP) with the R_34_Linknet network to expand the receptive field of the network and obtain a R_34_Linknet_ASPP network; Then the ASPP structure was improved to get a R_34_Linknet_ASPP+ network. The performance of the three networks were compared. R_34_Linknet_ASPP+ got the best performance, with 86.3% for F1, 94.7% for PA, 82.7% for MIoU, 89.0% for FWIoU, and 92.3% for MPA on the test set. The accuracy of apple orchard extraction in Wangdonggou, Changwu County and Tongji Village, Baishui County using R_34_Linknet_ASPP+ were 94.22% and 95.66%, respectively. In Wangdonggou, it was 1.21% and 0.58% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. In Tongji village, it was 1.70% and 0.90% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. The results show that the proposed R_34_Linknet_ASPP+ method can extract apple orchards accurately, the edge treatment of apple orchard plots is better, the method can be used as the technical support and theoretical basis for research on the spatial distribution mapping of apple orchards on the Loess Plateau. 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 （501）   HTML （131）    PDF（pc） （2531KB）（526）       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 （435）   HTML （136）    PDF（pc） （837KB）（521）       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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Design and Test of Self-Propelled Orchard Multi-Station Harvesting Equipment MIAO Youyi, CHEN Hong, CHEN Xiaobing, TIAN Haoyu, YUAN Dong Smart Agriculture    2022, 4 (3): 42-52.   DOI: 10.12133/j.smartag.SA202206007 Abstract （341）   HTML （44）    PDF（pc） （1391KB）（494）       Knowledge map    Save In order to solve the problems of high labor intensity, low efficiency of manual operation and lack of supporting machinery in the fruit harvesting of modern orchards, a self-propelled orchard multi-station harvesting equipment was designed in combination with the fruit tree dwarf anvil wide-row dense planting mode and agronomic planting requirements. The whole machine structure and working principle of the self-propelled orchard multi-station harvesting equipment were expounded. According to the environmental conditions of mountainous orchards, the crawler chassis structure was designed, and the working speed was 0~2 km/h. The operating platform including left extension platform and right extension platform was designed according to the difference of fruit tree row spacing, and the working width of the operating platform was 1500~2700 mm. In order to improve the working efficiency and ensure the same picking speed of upper and lower operators, the picking operation mode of "two sides, two heights and six stations" was proposed by comparing the difference in the working flexibility between the operator on the platform and the operator on the ground during the operation of the machine, and the in-and-out channels of fruit boxes and the automatic collection and packing device were designed. The front and rear unobstructed fruit box access system was composed of the front loading and unloading mechanism, the rear loading and unloading mechanism and the fruit box slide rail, which was convenient for the empty fruit box to enter the fruit loading station of the working platform from the front and unloading from the rear after the fruit was filled. Six sub-conveyor belts were designed to handle apples harvested by six non interacting operators at the same time. The prototype was test in the field, and the packing uniform distribution coefficient calculation method was proposed to evaluate the uniformity of fruit packing, and the performance of the prototype was comprehensively evaluated in combination with the fruit damage rate and packing speed. The results showed that, the designed self-propelled orchard multi-station harvesting equipment could synchronize with the six stations manual harvesting speed. At the same time, with the help of the expansion platform, the apple picking range covered the entire canopy of the fruit tree. The prototype worked smoothly, and the speed of each conveyor belt was in good coordination with manual picking, and there was no apple congestion occurred. The apple harvest damage rate was 4.67%, the packing uniform distribution coefficient was 1.475, and the packing speed was 72.9 apples per minute, which could meet the requirements of orchard harvest operation. Reference | Related Articles | Metrics | Comments（0）

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Evaluation and Countermeasures on the Development Level of Intelligent Cold Chain in China YANG Lin, YANG Bin, REN Qingshan, YANG Xinting, HAN Jiawei Smart Agriculture    2023, 5 (1): 22-33.   DOI: 10.12133/j.smartag.SA202302003 Abstract （370）   HTML （133）    PDF（pc） （1380KB）（491）       Knowledge map    Save The new generation of information technology has led to the rapid development of the intelligent level of the cold chain, and the precise control of the development level of the smart cold chain is the prerequisite foundation and guarantee to achieve the key breakthrough of the technical bottleneck and the strategic layout of the development direction. Based on this, an evaluation index system for China's intelligent cold chain development from the dimensions of supply capacity, storage capacity, transportation capacity, economic efficiency and informationization level was conducted. The entropy weight method combined with the technique for order preference by similarity to ideal solution (TOPSIS) was used to quantitatively evaluate the development of intelligent cold chain in 30 Chinese provinces and cities (excluding Tibet, Hong Kong, Macao and Taiwan) from 2017 to 2021. The quantitative evaluation of the level of intelligent cold chain development was conducted. The impact of the evaluation indicators on different provinces and cities was analysed by exploratory spatial data analyses (ESDA) and geographically weighted regression (GWR). The results showed that indicators such as economic development status, construction of supporting facilities and informationization level had greater weight and played a more important role in influencing the construction of intelligent cold chain. The overall level of intelligent cold chain development in China is divided into four levels, with most cities at the third and fourth levels. Beijing and the eastern coastal provinces and cities generally have a better level of intelligent cold chain development, while the southwest and northwest regions are developing slowly. In terms of overall development, the overall development of China's intelligent cold chain is relatively backward, with insufficient inter-regional synergy. The global spatial autocorrelation analysis shows that the variability in the development of China's intelligent cold chain logistics is gradually becoming greater. Through the local spatial autocorrelation analysis, it can be seen that there is a positive spatial correlation between the provinces and cities in East China, and negative spatiality in North China and South China. After geographically weighted regression analysis, it can be seen that the evaluation indicators have significant spatial and temporal heterogeneity in 2017, with the degree of influence changing with spatial location and time, and the spatial and temporal heterogeneity of the evaluation indicators is not significant in 2021. In order to improve the overall development level of China's intelligent cold chain, corresponding development countermeasures are proposed to strengthen the construction of supporting facilities and promote the transformation and upgrading of information technology. This study can provide a scientific basis for the global planning, strategic layout and overall promotion of China's intelligent cold chain. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Gait Phase Recognition of Dairy Cows based on Gaussian Mixture Model and Hidden Markov Model ZHANG Kai, HAN Shuqing, CHENG Guodong, WU Saisai, LIU Jifang Smart Agriculture    2022, 4 (2): 53-63.   DOI: 10.12133/j.smartag.SA202204003 Abstract （414）   HTML （28）    PDF（pc） （1428KB）（475）       Knowledge map    Save The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Extraction of Potato Plant Phenotypic Parameters Based on Multi-Source Data HU Songtao, ZHAI Ruifang, WANG Yinghua, LIU Zhi, ZHU Jianzhong, REN He, YANG Wanneng, SONG Peng Smart Agriculture    2023, 5 (1): 132-145.   DOI: 10.12133/j.smartag.SA202302009 Abstract （304）   HTML （57）    PDF（pc） （2649KB）（440）       Knowledge map    Save Crops have diverse structures and complex growth environments. RGB image data can reflect the texture and color features of plants accurately, while 3D data contains information about crop volume. The combination of RGB image and 3D point cloud data can achieve the extraction of two-dimensional and three-dimensional phenotypic parameters of crops, which is of great significance for the research of phenomics methods. In this study, potatoe plants were chosen as the research subject, and RGB cameras and laser scanners were used to collect 50 potato RGB images and 3D laser point cloud data. The segmentation accuracy of four deep learning semantic segmentation methods, OCRNet, UpNet, PaNet, and DeepLab v3+, were compared and analyzed for the RGB images. OCRNet, which demonstrated higher accuracy, was used to perform semantic segmentation on top-view RGB images of potatoes. Mean shift clustering algorithm was optimized for laser point cloud data processing, and single-plant segmentation of laser point cloud data was completed. Stem and leaf segmentation of single-plant potato point cloud data were accurately performed using Euclidean clustering and K-Means clustering algorithms. In addition, a strategy was proposed to establish a one-to-one correspondence between RGB images and point clouds of single-plant potatoes using pot numbering. 8 2D phenotypic parameters and 10 3D phenotypic parameters, including maximum width, perimeter, area, plant height, volume, leaf length, and leaf width, etc., were extracted from RGB images and laser point clouds, respectively. Finally, the accuracy of three representative and easily measurable phenotypic parameters, leaf number, plant height, and maximum width were evaluated. The mean absolute percentage errors (MAPE) were 8.6%, 8.3% and 6.0%, respectively, while the root mean square errors (RMSE) were 1.371 pieces, 3.2 cm and 1.86 cm, respectively, and the determination coefficients (R2) were 0.93, 0.95 and 0.91, respectively. The research results indicated that the extracted phenotype parameters can accurately and efficiently reflect the growth status of potatoes. Combining the RGB image data of potatoes with three-dimensional laser point cloud data can fully exploit the advantages of the rich texture and color characteristics of RGB images and the volumetric information provided by three-dimensional point clouds, achieving non-destructive, efficient, and high-precision extraction of two-dimensional and three-dimensional phenotype parameters of potato plants. The achievements of this study could not only provide important technical support for the cultivation and breeding of potatoes but also provide strong support for phenotype-based research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （161）   HTML （21）    PDF（pc） （1531KB）（428）       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 （238）   HTML （53）    PDF（pc） （4042KB）（427）       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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Evaluation System of China's Low-Carbon Cold Chain Logistics Development Level YANG Bin, HAN Jiawei, YANG Lin, REN Qingshan, YANG Xinting Smart Agriculture    2023, 5 (1): 44-51.   DOI: 10.12133/j.smartag.SA202301011 Abstract （315）   HTML （47）    PDF（pc） （707KB）（423）       Knowledge map    Save In recent years, China's cold chain logistics industry has entered a stage of rapid development. At the same time, with the increase of greenhouse gas emissions, green and low-carbon transformation has become a new feature and direction of high-quality and healthy development of the cold chain industry to meet the future development needs of China's low-carbon economy. In view of this, in order to ensure the scientificity of China's low-carbon cold chain logistics evaluation system, in this paper, 30 indicators from the four levels of energy transformation, technological innovation, economic efficiency, and national policy based on different relevant levels were first preliminarily determined, and finally 14 indicators for building China's low-carbon cold chain logistics development evaluation system through consulting experts and the possibility of data acquisition were determined. Data from 2017 to 2021 were selected to conduct a quantitative evaluation of the development level of low-carbon cold chain logistics in China. Firstly, the entropy weight method was used to analyze the weight and obstacle degree of different indicators to explore the impact of different indicators on the development of low-carbon cold chain logistics; Secondly, a weighted decision-making matrix was constructed based on the weights of different indicators, and the technology for order preference by similarity to ideal solution (TOPSIS) evaluation model was used to evaluate the development of low-carbon cold chain logistics in China from 2017 to 2021, in order to determine the development and changes of low-carbon cold chain logistics in China. The research results showed that among the 14 different indicators of the established evaluation system for the development of low-carbon cold chain logistics in China, the growth rate of the use of green packaging materials, the number of low-carbon technical papers published, the proportion of scientific research personnel, the growth rate of cold chain logistics demand for fresh agricultural products, and the reduction rate of hydrochlorofluorocarbon refrigerants account for a relatively large proportion, ranking in the top five, respectively reaching 0.1243, 0.1074, 0.1066, 0.0982, and 0.0716, accounting for more than half of the overall proportion. It has a significant impact on the development of low-carbon cold chain logistics in China. From 2017 to 2021, the development level of China's low-carbon cold chain logistics was scored from 0.1498 to 0.2359, with a year-on-year increase of about 57.5%, indicating that China's low-carbon cold chain logistics development level was relatively fast in the past five years. Although China's low-carbon cold chain logistics development has shown an overall upward trend, it is still in the development stage. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Design Optimization and Test of Air Supply System for Multi-Duct Sprayer GUO Jiangpeng, WANG Pengfei, LI Xinhao, YANG Xin, LI Jianping, BIAN Yongliang, XUE Chunlin Smart Agriculture    2022, 4 (3): 75-85.   DOI: 10.12133/j.smartag.SA202201015 Abstract （280）   HTML （26）    PDF（pc） （1708KB）（422）       Knowledge map    Save In view of the uneven distribution of airflow inside the multi-air-duct sprayer, the air flow caused by the air outlet is disturbed and the droplet can not be evenly deposited on the fruit tree canopy. In this research, the length parameter of the inner baffle plate of the multi-duct sprayer was optimized. The Computational Fluid Dynamics (CFD) was used to simulate and analyze the internal airflow of the air supply system of the multi-duct sprayer based on Star-CCM+ software. The standard deviations of the wind speed of the wind outlet 1~6 at different guide plates were 0.7468, 0.6776, 1.4441, 5.1305, 4.5768 and 0.8209, respectively. Among them, the standard deviations of wind speed value at Point 1, Point 2 and Point 6 were less than 1, indicating that the change of deflector length has little impact on the speed change. The standard deviations of wind speed value at Point 3, Point 4 and Point 5 were large, indicating that with the change of deflector length, the wind speed at Air outlet 3, Air outlet 4, Air outlet 5 were greatly affected. On this basis, through the response surface analysis of Air outlet 3, Air outlet 4 and Air outlet 5, it was determined that, the length of Deflector 1 as 200 mm, the length of Deflector 2 as 60 mm and the length of Deflector 3 as 50 mm, was the optimal parameter combination. Under the optimal combination parameters, the wind speed values of symmetrical Air outlet 3 and Air outlet 6 were 39.135 and 41.320 m/s, respectively, with a relative deviations of 5.58%. The wind speed values of air outlet 4 and air outlet 5 were 33.022 and 34.328 m/s, respectively, with a relative deviation of 3.95%, which meeting the design requirements of sprayer. The indoor wind speed test results showed that the average wind speed of the upper layer was 15.75 m/s, the average wind speed of the middle layer was 20.83 m/s, and the average wind speed of the lower layer was 28.27 m/s, which met the end speed principle. The wind field was distributed according to the shape of the fruit tree canopy. The wind field of the left and right sides of the sprayer was symmetrical distributed and the air distribution was uniform. The work can provide a reference for the design of multi-duct sprayer. 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 （283）   HTML （52）    PDF（pc） （1400KB）（411）       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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Development of Mobile Orchard Local Grading System of Apple Internal Quality LI Yang, PENG Yankun, LYU Decai, LI Yongyu, LIU Le, ZHU Yujie Smart Agriculture    2022, 4 (3): 132-142.   DOI: 10.12133/j.smartag.SA202206012 Abstract （302）   HTML （41）    PDF（pc） （1496KB）（393）       Knowledge map    Save The detecting and grading of the internal quality of apples is an effective means to increase the added value of apples, protect the health of residents, meet consumer demand and improve market competitiveness. Therefore, an apple internal quality detecting module and a grading module were developed in this research to constitute a movable apple internal quality orchard origin grading system, which could realize the detection of apple sugar content and apple moldy core in orchard origin and grading according to the set grading standard. Based on this system, a multiplicative effect elimination (MEE) based spectral correction method was proposed to eliminate the multiplicative effect caused by the differences in physical properties of apples and improve the internal quality detection accuracy. The method assumed that the multiplication coefficient in the spectrum was closely related to the spectral data at a certain wavelength, and divided the original spectrum by the data at this wavelength point to achieve the elimination of the multiplicative scattering effect of the spectrum. It also combined the idea of least-squares loss function to set the loss function to solve for the optimal multiplication coefficient point. To verify the validity of the method, after pre-processing the apple spectra with multiple scattering correction (MSC), standard normal variate transform (SNV), and MEE algorithms, the partial least squares regression (PLSR) prediction models for apple sugar content and partial least squares-discriminant analysis (PLS-DA) models for apple moldy core were developed, respectively. The results showed that the MEE algorithm had the best results compared to the MSC and SNV algorithms. The correlation coefficient of correction set (Rc), root mean square error of correction set (RMSEC), the correlation coefficient of prediction set (Rp), and root mean square error of prediction set (RMSEP) for sugar content were 0.959, 0.430%, 0.929, and 0.592%, respectively; the sensitivity, specificity, and accuracy of correction set and prediction set for moldy core were 98.33%, 96.67%, 97.50%, 100.00%, 90.00%, and 95.00%, respectively. The best prediction model established was imported into the system for grading tests, and the results showed that the grading correct rate of the system was 90.00% and the grading speed was 3 pcs/s. In summary, the proposed spectral correction method is more suitable for apple transmission spectral correction. The mobile orchard local grading system of apple internal quality combined with the proposed spectral correction method can accurately detect apple sugar content and apple moldy core. The system meets the demand for internal quality detecting and grading of apples in orchard production areas. Reference | Related Articles | Metrics | Comments（0）

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Comparison of Droplet Deposition Performance Between Caterpillar Mist Sprayer and Six-Rotor Unmanned Aerial Vehicle in Mango Canopy LI Yangfan, HE Xiongkui, HAN Leng, HUANG Zhan, HE Miao Smart Agriculture    2022, 4 (3): 53-62.   DOI: 10.12133/j.smartag.SA202207007 Abstract （333）   HTML （27）    PDF（pc） （1650KB）（378）       Knowledge map    Save In order to solve the problems of pesticides abuse, nonuniformity deposition and low operating efficiency, build up the smart mango orchard, sedimentary properties of liquids in mango canopy of two orchard pesticide machinery, i.e., orchard caterpillar mist sprayer and six-rotor unmanned aerial vehicle (UAV) of were compared. Mango canopy was divided into upper, middle and lower canopy, tartrazine wsa selected as the tracer, high-definition printing paper and filter paper were used to collect pesticide droplets, the image processing methods such as deposition distribution uniformity were used to analyze the droplets. The experimental results showed that, for the surface droplets coverage rate of upper canopy leaf, unmanned aerial vehicle (UAV) was significantly higher than the cartipillar mist sprayer, there was no significant difference for the middle and lower canopy leaf. The the average coverage rate of both the front and back of leaves in UAV treatment group were 1.5~2 times for cartipillar mist sprayer, and got more deposition in back of leaves compare with caterpillar mist sprayer. The density of droplets on the front of the leaves of the mist sprayer treatment was significantly higher than that of the UAV treatment, but there was no significant difference on the back of the leaves. Both the front and back of the leaves of the plant protection UAV did not meet the requirements of disease and pest control with a low spray amount of 20/cm2. The liquid deposition of mist sprayer concentrated in the middle and lower canopy (61.1%), and while for the UAVs, it concentrated in the upper canopy (43.0%). The proportion of the deposition in the canopy was higher than that of the UAVs (48.6%), but the deposition capacity of mist sprayer in the upper canopy was insufficient, accounting for only 17%. The research shows that, compared with UAV, caterpillar mist sprayer is more suitable for the pest control of lower and middlein canopy, at the same time, the high density of droplets cover also has obvious advantages when spraying fungicide. UAV is more suitable for the external tidbits pest control of upper mango canopy, such as thrips, anthrax. According to the experimental results, a stereoscopic plant protection system can be built up in which can use the advantages of both caterpillar mist sprayer and UAV to achieve uniform coverage of pesticide in the mango tree canopy. 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 （602）   HTML （153）    PDF（pc） （2498KB）（377）       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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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）（357）       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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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 （236）   HTML （42）    PDF（pc） （1175KB）（323）       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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Progressive Convolutional Net Based Method for Agricultural Named Entity Recognition JI Jie, JIN Zhou, WANG Rujing, LIU Haiyan, LI Zhiyuan Smart Agriculture    2023, 5 (1): 122-131.   DOI: 10.12133/j.smartag.SA202303001 Abstract （308）   HTML （31）    PDF（pc） （965KB）（303）       Knowledge map    Save Pre-training refers to the process of training deep neural network parameters on a large corpus before a specific task model performs a particular task. This approach enables downstream tasks to fine-tune the pre-trained model parameters based on a small amount of labeled data, eliminating the need to train a new model from scratch. Currently, research on named entity recognition (NER) using pre-trained language model (PLM) only uses the last layer of the PLM to express output when facing challenges such as complex entity naming methods and fuzzy entity boundaries in the agricultural field. This approach ignores the rich information contained in the internal layers of the model themselves. To address these issues, a named entity recognition method based on progressive convolutional networks has been proposed. This method stores natural sentences and outputs representations of each layer obtained through PLM. The intermediate outputs of the pre-trained model are sequentially convolved to extract shallow feature information that may have been overlooked previously. Using the progressive convolutional network module proposed in this research, the adjacent two-layer representations are convolved from the first layer, and the fusion result continues to be convolved with the next layer, resulting in enhanced sentence embedding that includes the entire information dimension of the model layer. The method does not require the introduction of external information, which makes the sentence representation contain richer information. Research has shown that the sentence embedding output of the model layer near the input contains more fine-grained information, such as phrases and phrases, which can assist with NER problems in the agricultural field. Fully utilizing the computational power already used, the results obtained can enhance the representation embedding of sentences. Finally, the conditional random field (CRF) model was used to generate the global optimal sequence. On a constructed agricultural dataset containing four types of agricultural entities, the proposed method's comprehensive indicator F1 value increased by 3.61% points compared to the basic BERT (Bidirectional Encoder Representation from Transformers) model. On the open dataset MSRA, the F1 value also increased to 94.96%, indicating that the progressive convolutional network can enhance the model's ability to represent natural language and has advantages in NER tasks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Design and Test of Portable Aflatoxin B1 Detection System WANG Pengfei, GAO Yuanyuan, LI Aixue Smart Agriculture    2023, 5 (1): 146-154.   DOI: 10.12133/j.smartag.SA202303004 Abstract （226）   HTML （24）    PDF（pc） （1224KB）（303）       Knowledge map    Save To achieve rapid on-site detection of aflatoxin B1 (AFB1) in agricultural and sideline products, a portable detection system based on differential pulse voltammetry (DPV) and STM32F103ZET6 as the core processor was designed. The system consists of two main parts: hardware detection devices and a mobile App, which are connected through Wi-Fi communication. The hardware detection equipment includes a DPV waveform generation circuit, constant potential circuit, and micro current detection module. The upper computer App was developed in an Android environment and completed tasks such as signal acquisition and data storage. After completing the design, experiments were conducted to verify the accuracy of the constant potential circuit and micro current detection module. The constant potential circuit accurately applied the voltage set by the program to the electrode, with a maximum error of 4 mV. The micro current detection module converts the current into a voltage signal according to the theoretical formula and amplifies it according to the theoretical amplification factor. The laboratory-made AFB1 sensor was used to effectively detect AFB1 in the range of 0.1 fg/ml to 100 pg/ml. The maximum relative error between the test results in the standard solution and the electrochemical workstation CHI760e was 7.37%. Furthermore, peanut oil samples with different concentrations of AFB1 were tested, and the results were compared to the CHI760e detection results as the standard, with a recovery rate of 96.8%~106.0%. Peanut samples with different degrees of mold were also tested and compared with CHI760e, with a maximum relative error of 7.10%.The system's portability allows it to be easily transported to different locations for on-site testing, making it an ideal solution for testing in remote or rural areas where laboratory facilities may be limited. Furthermore, the use of a mobile App for data acquisition and storage makes it easy to track and manage testing results. In summary, this portable detection system has great potential for widespread application in the rapid on-site detection of AFB1 in agricultural and sideline products. 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）（302）       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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Porphyrin and Semiconducting Single Wall Carbon Nanotubes based Semiconductor Field Effect Gas Sensor for Determination of Phytophthora Strawberries WANG Hui, CHEN Ruipeng, YU Zhixue, HE Yue, ZHANG Fan, XIONG Benhai Smart Agriculture    2022, 4 (3): 143-151.   DOI: 10.12133/j.smartag.SA202205006 Abstract （220）   HTML （17）    PDF（pc） （1344KB）（299）       Knowledge map    Save Phytophthora strawberries, as a kind of plant pathogenic fungi, can cause strawberry skin and crown rot without safe and effective treatment, which affect the economic benefits of planting strawberries. Therefore, it is urgent to use low-cost diagnostic methods to achieve early prevention. Strawberry plants infected with Phytophthora cactorum would release a unique organic volatile gas, 4-ethylphenol, with a concentration ranging from 1.12 to 22.56 mg/kg, which could be used as a marker gas for rapid diagnosis of the disease. In this study, semiconducting single-walled carbon nanotubes (SWNT) and field effect sensors (FET) were used to prepare semiconductor field effect gas sensors (SWNT/FET) without selectivity. And then the metal porphyrin MnOEP with high sensitivity and selectivity to 4-ethylphenol was immoblized on the SWNT's surface to obtain MnOEP-SWNT/FET. MnOEP-SWNT/FET has the advantages of low cost, low power consumption, small size, high sensitivity and easy integration, which can effectively overcome the shortcomings of gas chromatography-mass spectrometry, high-performance liquid chromatography and other analytical methods. By comparing the sensitivity and selectivity of different sensors, MnOEP-SWNT/FET is very suitable for real-time monitoring of 4-ethylphenol. The key reasons for the high sensitivity and selectivity are: MnOEP is a macromolecular heterocyclic compound formed by four pyrrole rings connected together by methylene and manganese ion(Mn), each pyrrole ring consists of four carbons and one nitrogen, and all nitrogen atoms inside the ring form a central cavity; the coordination metal ions of MnOEP are in an unsaturated state, gas molecules can interact with the central metal ions through van der Waals force and hydrogen bond at the axial position of MnOEP to change their own optical or electrical properties. MnOEP-SWNT/FET was studied by Raman spectrum, UV spectrum and voltammetry. The physical and chemical properties were analyzed and the detection conditions were optimized to improve the gas sensitivity of MnOEP-SWNT/FET to 4-ethylphenol. Under the optimal detection conditions, MnOEP-SWNT/FET has a good linear relationship with 0.25% ~100% saturated vapor of 4-ethylphenol (20 ℃) and the detection limit is 0.15% saturated vapor of 4-ethylphenol. The relative standard error of different concentrations was less than 10%. By measuring the actual samples, it has high detection accuracy for strawberry plants infected with Phytophthora, but it still exists false positive for healthy strawberry. 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 （323）   HTML （73）    PDF（pc） （3044KB）（276）       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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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 （256）   HTML （48）    PDF（pc） （1475KB）（256）       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 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）（247）       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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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 （146）   HTML （28）    PDF（pc） （1991KB）（224）       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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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 （156）   HTML （34）    PDF（pc） （2219KB）（221）       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 （111）   HTML （18）    PDF（pc） （1435KB）（213）       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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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）（200）       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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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 （190）   HTML （31）    PDF（pc） （2022KB）（200）       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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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）（180）       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 （161）   HTML （37）    PDF（pc） （1885KB）（167）       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 （158）   HTML （22）    PDF（pc） （2023KB）（157）       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 （155）   HTML （26）    PDF（pc） （2023KB）（153）       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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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 （77）   HTML （10）    PDF（pc） （1386KB）（139）       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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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 （93）   HTML （16）    PDF（pc） （1784KB）（138）       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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Agricultural Sensor: Research Progress, Challenges and Perspectives WANG Rujing Smart Agriculture    2024, 6 (1): 1-17.   DOI: 10.12133/j.smartag.SA202401017 Abstract （287）   HTML （54）    PDF（pc） （1179KB）（133）       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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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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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 （84）   HTML （14）    PDF（pc） （1384KB）（111）       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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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 （74）   HTML （11）    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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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 （100）   HTML （15）    PDF（pc） （870KB）（110）       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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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 （139）   HTML （24）    PDF（pc） （1735KB）（109）       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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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 （97）   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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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 （99）   HTML （37）    PDF（pc） （2408KB）（105）       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 （130）   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）（97）       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 （69）   HTML （13）    PDF（pc） （1719KB）（84）       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 （113）   HTML （24）    PDF（pc） （2011KB）（83）       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 （61）   HTML （13）    PDF（pc） （1147KB）（71）       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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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 （73）   HTML （13）    PDF（pc） （1344KB）（60）       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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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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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 （64）   HTML （11）    PDF（pc） （1068KB）（51）       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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