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Perspectives and experiences on the development and innovation of agricultural aviation and precision agriculture from the Mississippi Delta and recommendations for China Huang Yanbo Smart Agriculture    2019, 1 (4): 12-30.   DOI: 10.12133/j.smartag.2019.1.4.201909-SA003 Abstract （1856）   HTML （2133）    PDF（pc） （1240KB）（21063）       Knowledge map    Save Crop production management has advanced into the stage of smart agriculture, which is driven by state-of-the-art agricultural information technology, intelligent equipment and massive data resources. Smart agriculture inherits ideas from precision agriculture and brings agricultural production and management from mechanization and informalization to intelligentization with automatization. Precision agriculture has been developed from strategic monitoring operations in the 1980s to tactical monitoring and control operations in the 2010s. In its development, agricultural aviation has played a key role in serving systems for spray application of crop protection and production materials for precision agriculture with the guidance of global navigation through geospatial prescription mapping derived from remotely-sensed data. With the development of modernized agriculture, agricultural aviation is even more important for advancing precision agricultural practices with more efficient soil and plant health sensing and more prompt and effective system actuation and action. This paper overviews the status of agricultural aviation for precision agriculture to move toward smart agriculture, especially in the Mississippi Delta region, one of the most important agricultural areas in the U.S. The research and development by scientists associated with the Mississippi Delta region are reported. The issues, challenges and opportunities are identified and discussed for further research and development of agricultural aviation technology for next-generation precision agriculture and smart agriculture. Reference | Related Articles | Metrics | Comments（0）

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Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing Lan Yubin, Deng Xiaoling, Zeng Guoliang Smart Agriculture    2019, 1 (2): 1-19.   DOI: 10.12133/j.smartag.2019.1.2.201904-SA003 Abstract （4885）   HTML （6135）    PDF（pc） （1165KB）（6860）       Knowledge map    Save Rapid acquisition and analysis of crop information is the precondition and basis for carrying out precision agricultural practice. Variable spraying and agricultural operation management based on the actual degree of crop diseases, pests and weeds can reduce the cost of agricultural production, optimize crop cultivation, improve crop yield and quality, and thus achieve precise agricultural management. In recent years, with the rapid development of UAV industry, UAV agricultural remote sensing technologies have played an important role in monitoring crop diseases, insects and weeds because of high spatial resolution, strong timeliness and low cost. Firstly, this research introduces the basic idea and system composition of precision agricultural aviation, and the status of UAV remote sensing in precision agricultural aviation. Then, the common UAV remote sensing imaging and interpreting methods were discussed, and the progress of UAV agricultural remote sensing technologies in detecting crop diseases, pests and weeds were respectively expounded. Finally, the challenges in the development of UAV agricultural remote sensing technologies nowadays were summarized, and the future development directions of UAV agricultural remote sensing were prospected. This research can provide theoretical references and technical supports for the development of UAV remote sensing technology in the field of precision agricultural aviation. Reference | Related Articles | Metrics | Comments（0）

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Recent Advances and Future Outlook for Artificial Intelligence in Aquaculture LI Daoliang, LIU Chang Smart Agriculture    2020, 2 (3): 1-20.   DOI: 10.12133/j.smartag.2020.2.3.202004-SA007 Abstract （5455）   HTML （7180）    PDF（pc） （2843KB）（6627）       Knowledge map    Save The production of China's aquaculture has changed from extensive model to intensive model, the production structure is continuously adjusting and upgrading, and the production level has been continuously improved. However, as an important part of China's agricultural production, aquaculture plays an important role in promoting the development of China's agricultural economy. Low labor productivity, production efficiency and resource utilization, low-quality aquatic products, and the lack of safety guarantees have severely limited the rapid development of China's aquaculture industry. Using modern information technology and intelligent devices to realize precise, automated, and intelligent aquaculture, improving fishery productivity and resource utilization is the main way to solve the above contradictions. Artificial intelligence technology in aquaculture is to use the computer technology to realize the production process of aquaculture, monitor the growth of underwater organisms, judge, discuss and analyze problems, and then perform feeding, disease treatment, and breeding. In order to understand the development status and technical characteristics of artificial intelligence technology in aquaculture, in this article, five main aspects of aquaculture, i.e., life information acquisition, aquatic product growth regulation and decision-making, fish disease prediction and diagnosis, aquaculture environment perception and regulation, and aquaculture underwater robots, combined with the practical problems in aquaculture, were mainly focused on. The application principles and necessity of artificial intelligence technology in each aspect were explained. Commonly used technical methods were point out and the classic application cases were deeply analyzed. The main problems, bottlenecks and challenges in the current development of artificial intelligence technology in aquaculture were analyzed, including turbid water, multiple interference factors, corrosion of equipment, and movement of underwater animals, etc., and reasonable research directions for these potential challenges were pointed out. In addition, the main strategic strategies to promote the transformation of aquaculture were also proposed. The development of aquaculture is inseparable from artificial intelligence technology, this review can provide references to accelerate the advancement of digitalization, precision and intelligent aquaculture. Reference | Related Articles | Metrics | Comments（0）

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State-of-the-art and recommended developmental strategic objectivs of smart agriculture Zhao Chunjiang Smart Agriculture    2019, 1 (1): 1-7.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA005 Abstract （8739）   HTML （17280）    PDF（pc） （433KB）（6555）       Knowledge map    Save With the wide applications of modern information technology in agriculture, agricultural intelligent technology revolution with manifestation of smart agriculture is coming. Smart agriculture is an advanced stage in the development of agricultural informatization from digitalization to networking to intelligence, it forms a new way of agricultural production, i.e., taking information and knowledge as the core elements, and integrating modern information technology such as internet, internet of things (IoT), big data, cloud computing, artificial intelligence, intelligent equipment, and so on, to realize agricultural information perception, quantitative decision-making, intelligent control, precise input, and personalized service. Smart agriculture is a milestone in the development of agriculture and has become the development trend of modern agriculture in the world. In this article, the policies, measures, and programs for encouraging the development of smart agriculture issued by Japan, the European Union, the United Kingdom, Canada, the United States, and other countries and regions were summarized, the development history from 1.0 version to 4.0 version of agriculture and development status of smart agriculture in China were also analyzed: remarkable results has achieved, at the end of 2017, the proportion of internet access in administrative villages reached 96%, 204,000 villages established the AgroSciences Information Agency, the retail sales of rural networks reached RMB 1.25 trillion Yuan, 426 cost-effective agricultural IoT products and technologies have been formed by the implementation of IoT pilot project. Behind the rapid development, smart agriculture in China still faces the problems of lack of basic research and technology accumulation, technologies such as sensors for agriculture, animal and plant models with intelligent decision-making, intelligent and precise operation equipment are the main short-boards. The pilot construction projects for the application of smart agriculture have been carried out all over the country, however, the role of display was greater than the actual effect. In order to solving the problems and achieving development demand, the strategic objectives of breaking through the core technologies, realizing the three major changes of "machine replacing manpower", "computer replacing human brain", and "independent technology replacing imports", improving the agricultural production level of intelligence and management network, accelerating the popularization of information services, and reducing application cost, providing farmers with personalized and precise information services that are affordable, and well-used, greatly improving agricultural production efficiency, and guiding the development of modern agriculture were proposed. Based on the analysis above, finally, eight key tasks including developing agricultural sensors, large-load agricultural UAV (unmanned aerial vehicle) protection systems, smart tractors, agricultural robots, agricultural big data, agricultural artificial intelligence, integrated applications and smart agricultural industry, and five policy recommendations including strengthening government support, formulating relevant subsidy policies, strengthening technical standards, and opening data sharing for the future development of smart agriculture in China were proposed. Reference | Related Articles | Metrics | Comments（0）

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Research Status and Development Direction of Design and Control Technology of Fruit and Vegetable Picking Robot System WU Jianqiao, FAN Shengzhe, GONG Liang, YUAN Jin, ZHOU Qiang, LIU Chengliang Smart Agriculture    2020, 2 (4): 17-40.   DOI: 10.12133/j.smartag.2020.2.4.202011-SA004 Abstract （3677）   HTML （4156）    PDF（pc） （2346KB）（5106）       Knowledge map    Save Vegetable and fruit harvesting is the most difficult production process to achieve mechanized operations. High-efficiency and low-loss picking is also a worldwide problem in the field of agricultural robot research and development, resulting in few production and application equipment currently on the market. In response to the demand for picking vegetables and fruits, to improve the time-consuming, labor-intensive, low-efficiency, and low-automation problems of manual picking, scholars have designed a series of automated picking equipment in the recent 30 years, which has promoted the development of agricultural robot technology. In the research and development of fresh vegetable and fruit picking equipment, firstly, the harvesting object and harvesting scene should be determined according to the growth position, shape and weight of the crop, the complexity of the scene, the degree of automation required, through complexity estimation, mechanical characteristics analysis, pose modeling and other methods clarify the design requirements of agricultural robots. Secondly, as the core executor of the entire picking action, the design of the end effector of the picking robot is particularly important. In this article, the structure of the end effector was classified, the design process and method of the end effectors were summarized, the common end effector driving methods and cutting methods were expounded, and the fruit collection mechanism was summarized. Furthermore, the overall control scheme of the picking robot, recognition and positioning method, adaptive control scheme of obstacle avoidance method, quality classification method, human-computer interaction and multi-machine cooperation scheme were summarized. Finally, in order to evaluate the performance of the picking robot overall, the indicators of average picking efficiency, long-term picking efficiency, harvest quality, picking maturity rate and missed picking rate were proposed. The overall development trend was pointed that picking robots would develop toward generalization of picking target scenes, diversified structures, full automation, intelligence, and clustering were put forward in the end. Reference | Related Articles | Metrics | Comments（0）

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Agricultural Metaverse: Key Technologies, Application Scenarios, Challenges and Prospects CHEN Feng, SUN Chuanheng, XING Bin, LUO Na, LIU Haishen Smart Agriculture    2022, 4 (4): 126-137.   DOI: 10.12133/j.smartag.SA202206006 Abstract （1535）   HTML （244）    PDF（pc） （1045KB）（4107）       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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Developmental analysis and application examples for agricultural models Cao Hongxin, Ge Daokuo, Zhang Wenyu, Zhang Weixin, Cao Jing, Liang Wanjie, Xuan Shouli, Liu Yan, Wu Qian, Sun Chuanliang, Zhang Lingling, Xia Ji‘an, Liu Yongxia, Chen Yuli, Yue Yanbin, Zhang Zhiyou, Wan Qian, Pan Yue, Han Xujie, Wu Fei Smart Agriculture    2020, 2 (1): 147-162.   DOI: 10.12133/j.smartag.2020.2.1.202002-SA006 Abstract （2760）   HTML （1164）    PDF（pc） （2217KB）（3724）       Knowledge map    Save Agricultural models, agricultural artificial intelligent, and data analysis technology, etc., exist in whole processes of information perceiving, transmission, processing and control for smart agriculture， thus they are the core technology of smart agriculture. To furtherly make the substances and functions of agricultural models clear, facilitate its further research and application, drive smart agriculture development with healthy, steady, and sustainable, methods of systematic analysis, comparison, and chart for relationship, etc. were used in this research. The definition, classification, functions of the agricultural models were theoretically analyzed. The relationships between the agricultural models and the elements and processes of the smart agriculture were expounded, which made the functions of agricultural models clear, provided some agricultural models examples applied in the smart agriculture. The important studies and application progresses of agricultural models were reviewed. The comparison results of agricultural models showed that the 4 levels of agricultural biological elements, 6 scales of agricultural environmental elements, 6 administrative levels of agricultural technological and economic elements, and the relevant approaches for modeling agricultural system need to be considered. The research and application of multi-space scales on environment elements in the agricultural models would have the larger potential. The combination of agricultural models with molecular genetics, perceiving, and artificial intelligence, the collaboration among public and private researchers, and food security challenges have been an important power for further development of agricultural models, linking agricultural models with various agricultural system modeling, databases, harmonious and open data, and decision-making support systems (DSS) would be focus on. The research and application of the agricultural models in China have formed crop model series with Chinese characteristics, joined in the world trends of the Agricultural Model Intercomparison and Improvement Project (AgMIP), the smart agriculture, and so on. They should be speedy graspe chances and accelerate development. The agricultural models is a quantitative express of relationships within or among the agricultural system elements. An important method with epistemological values of quantifying and synthesizing agricultural sciences, and will play an indispensible role in data achieving and processing for the smart agriculture combining perceiving techniques, and become a significant bridge and bond. Reference | Related Articles | Metrics | Comments（0）

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Advances in the development and applications of intelligent equipment and feeding technology for livestock production Zhao Yiguang, Yang Liang, Zheng Shanshan, Xiong Benhai Smart Agriculture    2019, 1 (1): 20-31.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA017 Abstract （3032）   HTML （894）    PDF（pc） （990KB）（3686）       Knowledge map    Save Intelligent equipment for livestock production is one of the components of intelligent agricultural machinery equipment, and is the focus of technology development in international agricultural equipment industry. This paper reviewed the current situation and development trend of intelligent equipment for livestock production systems nationally and internationally, including electronic feeding stations, animal farming robots, and many supporting intelligent facilities within the animal house. The features and performance characteristics of the equipment were discussed. The development of intelligent equipment for livestock production systems mainly focused on pigs and dairy cows including electronic sow feeding station, lactating sow precision feeding system, electronic cattle feeding station, automatic cattle feeding system, cattle feed pusher and dairy cow milking robot. The development and application of intelligent livestock equipment such as the electronic feeding stations and feeding robots, have significantly increased the production efficiency and saved labor cost in both pig and dairy farms. In addition, it also contributed to improve both of the animal and farmer welfare. However, there is still considerable room to get the application of intelligent livestock equipment improved in practice. For example, the animals have to be trained to get used to the intelligent facilities. On the other hand, the intelligent facilities are also required to identify individual animal or animal organ more accurately in order to further increase the production efficiency. Therefore, the key features in the further development of intelligent livestock equipment would be smarter, more convenient, more reliable, and more economical. At the meantime, it should be a highly integrated and coordinated intelligent system including intelligent facilities, well trained staff, good animal welfare, and comfortable environment. Therefore, the industrial application of the intelligent livestock equipment should be integrated with the local farming practice and fitted with the layout of animal houses in order to increase the efficiency of the equipment, and consequently, to improve animal welfare. The systematical combination of intelligent facilities and animal physiology, animal growth, and animal behavior could contribute to the dynamic interactions between the equipment and animal. Finally, it was concluded that the development of intelligent equipment should be coordinated with the theory of animal production, the function of animal products and the innovation of farming practice. And it also should be continuously updated to promote the transformation and upgrading of animal husbandry industry. 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 （2586）   HTML （636）    PDF（pc） （2582KB）（3525）       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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Technical demands for agricultural remote sensing satellites in China Chen Zhongxin, Hao Pengyu, Liu Jia, An Meng, Han Bo Smart Agriculture    2019, 1 (1): 32-42.   DOI: 10.12133/j.smartag.2019.1.1.201901-SA003 Abstract （3141）   HTML （2900）    PDF（pc） （572KB）（3459）       Knowledge map    Save With the development of China's modern agriculture, information agriculture and smart agriculture, and the implementation of national rural revitalization strategy, there are very strong demands for timely and effective retrieving information for agricultural environment, production conditions, status, and procedure. Because of the inherent characteristics of agriculture, satellite remote sensing is one of the critical techniques in agricultural information acquisition. Based on the analysis of the applications of agricultural remote sensing satellites abroad and in China, the authors analyzed the technical demand and engineering demand of China's remote sensing satellites development according to the demand of modern agricultural development, in order to provide suggestions for the construction agricultural remote sensing satellite system in the national digital agriculture system. In developed economies, remote sensing satellites that can be used for agricultural applications have formed constellations or systems for integrative observation. Their designs of payloads and sensors onboard remote sensing satellites have taken full account of the demand for agricultural applications. Their technical innovation and information retrieval capability have been greatly enhanced in agricultural applications of satellite remote sensing. In contrast with that in the advanced foreign countries, the agricultural satellite remote sensing applications in China have quite a few problems and shortcomings. We rely mainly multi-spectral remote sensing systems, which leads to inadequate observation elements in agricultural remote sensing applications. Limited by the performance of remote sensing sensors and the inadequate ability of remote sensing satellite ground application system, there is a certain gap between quantitative remote sensing monitoring means in China and foreign developed countries. Based on a comprehensive analysis of the current and future demands of agricultural remote sensing applications in China, this paper suggests the agricultural requirements for the application capability and equipment of remote sensing satellites. It is suggested that a constellation system of agricultural satellites flying in a tandem sequence should be constructed. The constellation has multi-spectral, hyperspectral, infrared and microwave sensors, which can acquire the comprehensive features of the same objects in the same temporal phase, and thus obtain the data with high spatial-temporal consistency and consistency of solar illumination conditions. The precision of multi-source data fusion can comprehensively provide multi-scale remote sensing products with different bands, different polarization, active/passive, microwave/optical fusion. With help of this advanced agricultural remote sensing satellite system and national spatial infrastructure in China, it will enhance the capability to promote the rapid development of agricultural remote sensing technology and the integration of three-dimensional space-air-ground based digital agriculture 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）（3270）       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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Airborne remote sensing systems for precision agriculture applications Yang Chenghai Smart Agriculture    2020, 2 (1): 1-22.   DOI: 10.12133/j.smartag.2020.2.1.201909-SA004 Abstract （2433）   HTML （7171）    PDF（pc） （1726KB）（3218）       Knowledge map    Save Remote sensing has been used as an important data acquisition tool for precision agriculture for decades. Based on their height above the earth, remote sensing platforms mainly include satellites, manned aircraft, unmanned aircraft systems (UAS) and ground-based vehicles. A vast majority of sensors carried on these platforms are imaging sensors, though other sensors such as lidars can be mounted. In recent years, advances in satellite imaging sensors have greatly narrowed the gaps in spatial, spectral and temporal resolutions with aircraft-based sensors. More recently, the availability of UAS as a low-cost remote sensing platform has significantly filled the gap between manned aircraft and ground-based platforms. Nevertheless, manned aircraft remain to be a major remote sensing platform and offer some advantages over satellites or UAS. Compared with UAS, manned aircraft have flexible flight height, fast speed, large payload capacity, long flight time, few flight restrictions and great weather tolerance. The first section of the article provided an overview of the types of remote sensors and the three major remote sensing platforms (i.e., satellites, manned aircraft and UAS). The next two sections focused on manned aircraft-based airborne imaging systems that have been used for precision agriculture, including those consisting of consumer-grade cameras mounted on agricultural aircraft. Numerous custom-made and commercial airborne imaging systems were reviewed, including multispectral, hyperspectral and thermal cameras. Five application examples were provided in the fourth section to illustrate how different types of remote sensing imagery have been used for crop growth assessment and crop pest management for practical precision agriculture applications. Finally, some challenges and future efforts on the use of different platforms and imaging systems for precision agriculture were briefly discussed. Reference | Related Articles | Metrics | Comments（0）

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Progress and prospects of crop diseases and pests monitoring by remote sensing Huang Wenjiang, Shi Yue, Dong Yingying, Ye Huichun, Wu Mingquan, Cui Bei, Liu Linyi Smart Agriculture    2019, 1 (4): 1-11.   DOI: 10.12133/j.smartag.2019.1.4.201905-SA005 Abstract （3714）   HTML （7138）    PDF（pc） （566KB）（3176）       Knowledge map    Save Global change and natural disturbances have already caused a severe co-epidemic of crop pests and diseases, such as aphids, fusarium, rust, and powdery mildew. These threats may result in serious deterioration of grain yield and quality. Traditionally, crop pests and diseases are monitored by visual inspection of individual plants, which is time-consuming and inefficient. Besides, the distribution of different infected wheat patches are hard to identify through manual scouting. However, the spatial scale difference of remote sensing observation directly affects the remote sensing diagnosis mechanism and monitoring method of pests and diseases. The differences in pest and disease characterization and monitoring mechanisms promote the development of the remote sensing-based monitoring technology at different spatial scales, and the complementarity of multi-spatial data sources (remote sensing, meteorology, plant protection, etc.) increase the chance of the precision monitoring of the occurrence and development of pest and disease. As a non-destructive way of collecting ground information, remote sensing technologies have been proved to be feasible in crop pests and diseases monitoring and forecasting. Meanwhile, many crop diseases and pests monitoring and alarming systems have been developed to manage and control agricultural practices. Based on the description of physiological mechanism that crop diseases and pests stressed spectral response, some effective spectral wavelengths, remote sensing monitoring technologies, and crop pests and disease monitoring and forecasting system were summarized and sorted in this paper. In addition, challenge problems of key technology on monitoring crop diseases and pests with remote sensing was also pointed out, and some possible solutions and tendencies were also provided. This article detailed revealed the researches on the remote sensing based monitoring methods on detection and classification of crop pests and diseases with the challenges of regional-scale, multi-source, and multi-temporal data. In addition, we also reviewed the remote sensing monitoring of pests and diseases that meet the characteristics of different remote sensing spatial scale data and precise plant protection and control needs. Finally, we investigated the current development of the pest and disease monitoring systems which integrated the research and application of the existing crop pest and disease monitoring and early warning model. In summary, this review will prove a new perspective for sustainable agriculture from the current researches, thus, new technology for earth observation and habitat monitoring will not only directly benefit crop production through better pest and disease management but through the biophysical controls on pest and disease emergence. Application of UAVs, image processing to insect/disease detection and control should be directly transferable to other pests and diseases, with feedbacks into UAV and EO capabilities for the mapping and management of these agricultural risks. Similarly, these vision systems open other possibilities for farm robotics such as mechanical rather than manual pesticide usage for below crop canopy pest surveying. Reference | Related Articles | Metrics | Comments（0）

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Research progress and developmental recommendations on precision spraying technology and equipment in China He Xiongkui Smart Agriculture    2020, 2 (1): 133-146.   DOI: 10.12133/j.smartag.2020.2.1.201907-SA002 Abstract （2739）   HTML （4608）    PDF（pc） （871KB）（3032）       Knowledge map    Save Chemical plant protection, which refers to using plant protection machinery sprays chemical pesticides, is the most important technology for pest and disease control at present, an important technical guarantee for food security, and also is essential for safeguarding agricultural production. Pesticide, spray technology and plant protection machinery are called the three pillars of chemical plant protection, which having been becoming a hot research topic in the world. Efficient, precise and intelligent spray technology and equipment can provide guarantee for the improvement of pesticide efficacy and utilization. With the issues of agricultural product safety and environmental protection getting more and more attention from the public, the research and development direction of Chinese plant protection field will gradually turn to intelligent and precision spraying technology and equipment. Since 2010 year, the great development potential and application value of intelligent and precision spraying technologies and equipment have been widely recognized worldwide. In this article, the main precision spraying technologies were reviewed, the research status, typical representative and application progress of plant protection equipment in different working scenarios were classified and summarized. The challenges in the development of precision spraying were analyzed, the countermeasures and suggestions were put forward. This research can provide new methods and new ideas not only for implementation of China's pesticide reduction plan, the promotion of intelligent plant protection equipment and precision spraying technology, but for the development of modern agriculture. Reference | Related Articles | Metrics | Comments（0）

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Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases SHAO Mingyue, ZHANG Jianhua, FENG Quan, CHAI Xiujuan, ZHANG Ning, ZHANG Wenrong Smart Agriculture    2022, 4 (1): 29-46.   DOI: 10.12133/j.smartag.SA202202005 Abstract （3109）   HTML （529）    PDF（pc） （1061KB）（3026）       Knowledge map    Save Accurate detection and recognition of plant diseases is the key technology to early diagnosis and intelligent monitoring of plant diseases, and is the core of accurate control and information management of plant diseases and insect pests. Deep learning can overcome the disadvantages of traditional diagnosis methods and greatly improve the accuracy of diseases detection and recognition, and has attracted a lot of attention of researchers. This paper collected the main public plant diseases image data sets all over the world, and briefly introduced the basic information of each data set and their websites, which is convenient to download and use. And then, the application of deep learning in plant disease detection and recognition in recent years was systematically reviewed. Plant disease target detection is the premise of accurate classification and recognition of plant disease and evaluation of disease hazard level. It is also the key to accurately locate plant disease area and guide spray device of plant protection equipment to spray drug on target. Plant disease recognition refers to the processing, analysis and understanding of disease images to identify different kinds of disease objects, which is the main basis for the timely and effective prevention and control of plant diseases. The research progress in early disease detection and recognition algorithm was expounded based on depth of learning research, as well as the advantages and existing problems of various algorithms were described. It can be seen from this review that the detection and recognition algorithm based on deep learning is superior to the traditional detection and recognition algorithm in all aspects. Based on the investigation of research results, it was pointed out that the illumination, sheltering, complex background, different disorders with similar symptoms, different changes of disease symptoms in different periods, and overlapping coexistence of multiple diseases were the main challenges for the detection and recognition of plant diseases. At the same time, the establishment of a large-scale and more complex data set that meets the specific research needs is also a difficulty that need to face together. And at further, we point out that the combination of the better performance of the neural network, large-scale data set and agriculture theoretical basis is a major trend of the development of the future. It is also pointed out that multimodal data can be used to identify early plant diseases, which is also one of the future development direction. Reference | Related Articles | Metrics | Comments（0）

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Advances and Progress of Agricultural Machinery and Sensing Technology Fusion CHEN Xuegeng, WEN Haojun, ZHANG Weirong, PAN Fochu, ZHAO Yan Smart Agriculture    2020, 2 (4): 1-16.   DOI: 10.12133/j.smartag.2020.2.4.202002-SA003 Abstract （3939）   HTML （7684）    PDF（pc） （2650KB）（3008）       Knowledge map    Save Agricultural machinery and equipment are important foundations for transforming agricultural development methods and promoting sustainable agricultural development, as well as are the key areas and core supports for promoting agricultural modernization. In order to clarify the development ideas of agricultural machinery informatization and find the key development directions, and vigorously promote the development of agricultural machinery intelligentization, the development status of foreign agricultural machinery and sensing technology fusion were analyzed in this article, and five major development characteristics: 1) development towarding digitalization, automation and informationization, 2) applying sensing technology to the design and manufacturing of agricultural machinery equipment, 3) rapidly developing of animal husbandry machinery sensing technology, 4) focusing on resource conservation and environmental protection, and sensing technology promoting sustainable agricultural development, and 5) towarding intelligent control, automatic operation and driving comfort development were summarized. Among them, some advanced intelligent agricultural machinery were introduced, including the German Krone BiGX700 self-propelled silage harvester, an automatic weeding and fertilization robot developed by the Queensland University of Technology in Australia—Agbot II, and John Deere CP690 self-propelled baler Cotton machine, etc. After that, the new characteristics of the development of agricultural mechanization in China were summarize, and the viewpoint was pointed out that although the current development of agricultural mechanization in China had achieved remarkable results, there were still problems such as low intelligence and informatization of agricultural machinery, and insufficient fusion of agricultural machinery and informatization. Then the prospects for the development of China's agricultural machinery and sensing technology fusion were put forward, including 1) promoting the development of intelligent perception technology and navigation technology research, 2) promoting the intelligentization of agricultural machinery and equipment, and building an agricultural intelligent operation system, 3) promoting the research of agricultural machinery autonomous operation technology and the construction of unmanned farms, and 4) strengthening the technical standard formulation of agricultural machinery informatization and the training of compound talents. The fusion of agricultural machinery and sensing technology can realize the effective and diversified fusion of agricultural mechanization and sensing technology, maximize the guiding effect of informatization, improve the efficiency of agricultural production in China, and promote the development of digital agriculture and modern agriculture. Reference | Related Articles | Metrics | Comments（0）

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Research progress and prospect on non-destructive detection and quality grading technology of apple Cao Yudong, Qi Weiyan, Li Xian, Li Zhemin Smart Agriculture    2019, 1 (3): 29-45.   DOI: 10.12133/j.smartag.2019.1.3.201906-SA011 Abstract （1911）   HTML （1083）    PDF（pc） （1394KB）（2968）       Knowledge map    Save China has high total apple output, but the export volume is low. The high-end apple market is mostly occupied by imported apples. The main reason of this situation is the lack of technologies and equipments for fruit quality classification, and the degree of automation after picking stands low. The apples enter the consumer market without simple roughing processing, and the quality of the apple is unstable, which greatly reduces its market competitiveness. In this paper, the status quo of non-destructive detection and grading technology of apple quality was analyzed, then the development was forecasted. Apple non-destructive detection technology mainly includes spectrum, electrical characteristics, CT, chromatography, electronic nose and computer vision technology. According to the functional characteristics, advantages and disadvantages of various technologies, it is proposed to develop apple odor detection method based on new sensor technology; adopting multi-feature grading method based on machine vision, the combination of apple quality non-destructive testing technology and grading technology can promote the improvement of apple's industrial competitiveness. Overall, the needs of apple quality non-destructive detection and grading technology development in China are urgent. Detections with new technologies such as nanotechnology, biotechnology and artificial intelligence methods of sensor technology and products in apple non-destructive, quality grading detection and multi-technology have great potential. A real-time, efficient, high-precision grading systems in apple quality which integrates electricity, light, gas and computer vision may be an important development direction for improving apple's quality and enhancing the competitiveness of the apple industry. Reference | Related Articles | Metrics | Comments（0）

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Key technology analysis and research progress of UAV intelligent plant protection Xu Min, Zhang Ruirui, Chen Liping, Tang Qing, Xu Gang Smart Agriculture    2019, 1 (2): 20-33.   DOI: 10.12133/j.smartag.2019.1.2.201812-SA025 Abstract （2793）   HTML （2422）    PDF（pc） （1405KB）（2949）       Knowledge map    Save UAV plant protection operation faces very complicated environmental conditions. On one hand, its ultra low altitude operations are vulnerable to ground structures and basic hydropower facilities; on the other hand, the effectiveness of plant protection operation is strong, and it is necessary to spray the pesticides to the specific parts of crops at the prescribed time so as to ensure good pesticide application effect. At present, UAV plant protection technology mainly refers to the existing mature technology and flight platform in general aviation field to basically "fly and spray". However, the lack of penetrating research and theoretical guidance on environmental perception in farmland operation, the movement mechanism of droplets under the rotor airflow, and the penetrability of the droplet to different crops canopy lead to low penetration rate of the UAV plant protection operation, easy drifting, frequent accidents, large damage probability and low comprehensive operational efficiency. Benefiting from the breakthroughs in artificial intelligence, parallel computing technology and intelligent hardware, the UAV plant protection technology is developing in the direction of intellectualization, systematization and precision. The real-time perception of the environment under non established conditions, intelligent job decision method based on intelligent recognition of crop diseases and pests, the control of the toward-target pesticide spraying control based on the variable of wind field droplet deposition model and the data based job evaluation system have gradually become the key technology of the UAV intelligent plant protection. The manuscript analyzed and summarized the research status and technical achievements in the field of UAV intelligent plant protection from the field information perception, the modeling and optimization control of accurate pesticide application, the evaluation and monitoring of the operation effect. Based on the existing research, the research also predicted the development trend of the key technologies of intelligent UAV plant protection in the future. The clustering method of hyper-spectral image acquisition and computational intelligence based deep learning recognition will become the key technology for real-time and efficient acquisition of crop target information in plant protection work, which greatly improves the accuracy of remote sensing information inversion recognition; machine vision and multi machine cooperative sensing technology can acquire dynamic information of field operation at multiple levels and time; the high precision droplet spectrum control technology independently controlled by nozzle design and the precision variable spraying control technology based on the wind field model can further improve the droplet deposition effect and reduce the liquid drifting; the breakthrough of high accuracy mesh solution technology will change the prediction mode of droplet drift from artificial experience judgment to computer simulation and numerical deduction; the job path planning technology will greatly improve the efficiency of multi machine and multi area operation and reduce the distance of invalid operation; the job quality evaluation based on the real-time data of the sensor and the operation supervision system of large data technology will replace people to effectively control the process of the UAV plant protection operation, achieve data and transparency of plant protection, and ensure the process is observable and controllable. Reference | Related Articles | Metrics | Comments（0）

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Information sensing and environment control of precision facility livestock and poultry farming Teng Guanghui Smart Agriculture    2019, 1 (3): 1-12.   DOI: 10.12133/j.smartag.2019.1.3.201905-SA006 Abstract （2676）   HTML （2571）    PDF（pc） （2880KB）（2917）       Knowledge map    Save The fine breeding of livestock and poultry facilities is the frontier of the development of modern animal husbandry. The core of the fine breeding of livestock and poultry facilities lies in the deep integration of the "Internet of Things+" with traditional farming facilities. In recent years, with the withdrawal of more and more individual family-based breeding models, the management methods of livestock and poultry farms in China have gradually moved towards intensification, large scale,and automated facilitation. The traditional family-style livestock and poultry management experience is falling behind and gradually withdrawing from the historical stage. The refined farming of livestock and poultry facilities based on the individual animal management and quality assurance of farmed animals and animal welfare requirements have become the latest development trend of livestock and poultry farming industry. The rapid development of digital and network technology will provide new opportunities for the organic combination of animal husbandry production, animal welfare, information management and sustainable utilization of natural resources. Economic benefit, animal health and welfare, refinement of production process management and product quality are three key factors that affect the sustainable development of animal husbandry. In this paper, based on expounding the importance of the information sensing and the environmental regulation and control of the fine breeding livestock and poultry facilities, a cutting-edge technology of the information sensing and the environmental regulation and control of the livestock and poultry facilities was introduced; problems and challenges to be faced with were analyzed; and it was concluded that the smart sensor technology would become the base driving force for progress of livestock and fine poultry breeding facilities, taking account of the welfare of livestock and animal performance of animal anthropomorphizing intelligent control technology and strategy is facing significant challenges. In the field of pig farming, the core direction is mechanized production mode, which is light simplification, feed hygiene and animal health. In the field of cattle farming, the main direction is the automation of the whole chain of forage and the safety of its enclosure facilities. In the field of milking technology, the frontier of technological innovation is to further improve milking efficiency and quality, milking process, low disturbance milk metering, and cow individual milk production prediction. In the field of poultry production, similar to cattle farming, more attention is paid to the improvement of engineering processes such as bedding, environment and drinking water. Finally the paper put forward suggestions on how to implement the key technologies of fine farming of livestock and poultry facilities in China, with purpose of providing theoretical reference and technical support for the transformation, upgrading sustainable development of livestock and poultry breeding industry. Reference | Related Articles | Metrics | Comments（0）

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Method for identifying crop disease based on CNN and transfer learning Li Miao, Wang Jingxian, Li Hualong, Hu Zelin, Yang XuanJiang, Huang Xiaoping, Zeng Weihui, Zhang Jian, Fang Sisi Smart Agriculture    2019, 1 (3): 46-55.   DOI: 10.12133/j.smartag.2019.1.3.201903-SA005 Abstract （2573）   HTML （2302）    PDF（pc） （2845KB）（2574）       Knowledge map    Save The internet is a huge resource base and a rich knowledge base. Aiming at the problem of small agricultural samples, the utilization technology of network resources was studied in the research, which would provide an idea and method for the research and application of crop disease identification and diagnosis. The knowledge transfer and deep learning methods to carry out research and experiments on public data sets (ImageNet, PlantVillage) and laboratory small sample disease data (AES-IMAGE) were introduced: first the batch normalization algorithm was applied to the AlexNet and VGG of Convolutional Neural Network (CNN) models to improve the over-fitting problem of the network; second the transfer learning strategy using parameter fine-tuning: The PlantVillage large-scale plant disease dataset was used to obtain the pre-trained model. On the improved network (AlexNet, VGG model), the pre-trained model was adjusted by our small sample dataset AES-IMAGE to obtain the disease identification model of cucumber and rice; third the transfer learning strategy was used for the bottleneck feature extraction: using the ImageNet big dataset to obtain the network parameters, CNN model (Inception-v3 and Mobilenet) was used as feature extractor to extract disease features. This method requires only a quick identification of the disease on the CPU and does not require a lot of training time, which can quickly complete the process of disease identification on the CPU. The experimental results show that: first in the transfer learning strategy of parameter fine-tuning: the highest accuracy rate was 98.33%, by using the VGG network parameter fine-tuning strategy; second in the transfer learning strategy of bottleneck feature extraction, using the Mobilenet model for bottleneck layer feature extraction and identification could obtain 96.8% validation accuracy. The results indicate that the combination of CNN and transfer learning is effective for the identification of small sample crop diseases. Reference | Related Articles | Metrics | Comments（0）

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Application analysis and suggestions of modern information technology in agriculture: Thoughts on Internet enterprises entering agriculture Kong Fantao, Zhu Mengshuai, Sun Tan Smart Agriculture    2019, 1 (4): 31-41.   DOI: 10.12133/j.smartag.2019.1.4.201906-SA012 Abstract （2897）   HTML （10021）    PDF（pc） （964KB）（2486）       Knowledge map    Save With the rapid development of information technology and the steady growth of the agricultural and rural economy, agricultural information technology has attracted more and more attention, and the trend of capital and technology playing important roles in the agricultural field has gradually formed. In recent years, large Internet enterprises have begun to enter the agricultural industry and smart agriculture has developed strongly. This paper analyzed the status and technical application characteristics of large-scale Internet companies engaged in agriculture; explained the reasons why the current technology and capital entered the agricultural field in large numbers, especially in the context of the world science and technology revolution and China's economic and social status, analyzed the key areas and problems of the combination of technology, capital and agricultural industry; analyzed the application boundary, application prospects of information technology in the agricultural field. In view of the digital development and application of new technology in agricultural and rural areas, this paper put forward some policy suggestions. Firstly, strengthen policy guidance and support to prevent market speculation risks; secondly, built a system and mechanism for the convergence and integration of Internet enterprises and agricultural industries; thirdly, focus on cutting-edge key technologies and strengthen efforts to promote scientific and technological innovation; finally speed up the dynamic follow-up of technology achievement transformation, strengthen supervision and do a good job in leading and demonstration drive. The key priority is to focus on the world’s cutting-edge technology and key application technology, strengthen the dominant position of technological innovation of enterprises, and combine with the specific practice of production, circulation and consumption of China’s agricultural industry to fully promote the innovation and application of China’s agricultural information technology. And the main research contents included summarize the successful examples carefully, doing a good job in publicity and guidance, and promoting the typical leads vigorously so that they can be copied, popularized and applied; for the failure cases, learn from the insufficient lessons to prevent the recurrence of similar cases; for the advanced practical technology formed by Internet enterprises, promote technology sharing and information sharing on the premise of protecting intellectual property rights and turn it into a new driving force for the development of agricultural modernization. Only by applying the latest achievement of modern information technology to the practice of agricultural production and becoming the representative of agricultural productivity, can we truly contribute to the development of modern agriculture and rural areas in China and the wing of information. Reference | Related Articles | Metrics | Comments（0）

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Corn plant disease recognition based on migration learning and convolutional neural network Chen Guifen, Zhao Shan, Cao Liying, Fu Siwei, Zhou Jiaxin Smart Agriculture    2019, 1 (2): 34-44.   DOI: 10.12133/j.smartag.2019.1.2.201812-SA007 Abstract （2234）   HTML （699）    PDF（pc） （4817KB）（2409）       Knowledge map    Save Corn is one of the most important food crops in China, and the occurrence of disease will result in serious yield reduction. Therefore, the diagnosis and treatment of corn disease is an important link in corn production. Under the background of big data, massive image data are generated. The traditional image recognition method has a low accuracy in identifying corn plant diseases, which is far from meeting the needs. With the development of artificial intelligence and deep learning, convolutional neural network, as a common algorithm in deep learning, is widely used to deal with machine vision problems. It can automatically identify and extract image features. However, in image classification, CNN still has problems such as small sample size, high sample similarity and long training convergence time. CNN has the limitations of expression ability and lack of feedback mechanism, and data enhancement and transfer learning can solve the corresponding problems. Therefore, this research proposed an optimization algorithm for corn plant disease recognition based on the convolution neural network recognition model combining data enhancement and transfer learning. Firstly, the algorithm preprocessed the data through the data enhancement method to expand the data set, so as to improve the generalization and accuracy of the model. Then, the CNN model based on transfer learning was constructed. The Inception V3 model was adopted through transfer learning to extract the image characteristics of the disease while keeping the parameters unchanged. In this way, the training process of the convolutional neural network was accelerated and the over-fitting degree of the network was reduced. The extracted image features were used as input of the CNN to train the network, and finally the recognition results were obtained. Finally, the model was applied to the pictures of corn diseases collected from the farmland to accurately identify five kinds of corn diseases. Identification test results showed that using data to enhance the CNN optimization algorithm and the migration study on the average recognition accuracy main diseases of com (spot, southern leaf blight, gray leaf spot, smut, gall smut) reached 96.6%, which compared with single CNN, has greatly improved the precision and identification precision by 25.6% on average. The average processing time of each image was 0.28 s, shortens nearly 10 times than a single convolution neural network. The experimental results show that the algorithm is more accurate and faster than the traditional CNN, which provides a new method for identification of corn plant diseases. Reference | Related Articles | Metrics | Comments（0）

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Framework and recommendation for constructing the SAGI digital agriculture system Wu Wenbin, Shi Yun, Zhou Qingbo, Yang Peng, Liu Haiqi, Wang Fei, Liu Jia, Wang Limin, Zhang Baohui Smart Agriculture    2019, 1 (2): 64-72.   DOI: 10.12133/j.smartag.2019.1.2.201812-SA021 Abstract （1498）   HTML （1331）    PDF（pc） （665KB）（2391）       Knowledge map    Save The human society is entering the era of big data and data is becoming one of the key production elements. It is thus critical to develop the China's data-driving digital agriculture system, which would greatly promote the construction of digital China, stimulate the agriculture high-quality development and improve the agricultural competitiveness at the global market. To achieve this goal, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. This research, from the perspective of agricultural information science, describes the new framework of satellite, aerial, and ground integrated (SAGI) digital agriculture system for comprehensive agricultural monitoring, modeling, and management. The SAGI system differs from traditional digital agriculture systems and includes 5 important functionalities which are resource survey, production controlling, disaster monitoring, market early-warning and decision supporting. To make the system running in operation, it is necessary to first build an observation system, which integrates the satellite, aerial, and ground in-situ observation systems to capture more sophisticated, accurate and reliable data at different scales. The system is extremely needed for China, a large country with a great geographic difference, diverse agricultural cultivation and multiple agricultural traditions. This observing system helps to form the agricultural big data for subsequent data analysis and data mining. Secondly, using the big data collected, 4 key digitalization and monitoring tasks targeting at resource property right, production process, natural disaster and market status should be implemented so as to transform the data to knowledge. In this process, some diagnosis algorithms and models are developed to understand the growth and health of crops and animals, as well as their interaction with the agro-environment. With the above support, a management system covering the full range of agricultural production, processing, selling, management and services should be established to provide the rapid and reliable information support to decision-making as well to the local farming management, thereby guaranteeing agricultural sustainability and national food security. Thirdly, some key fields for future science and technology innovation to support the applications of the SAGI system should to be enhanced such as the standardization designing, innovation in technologies and instruments, system integration and platform development. Finally, considering the complicated and integrative characteristics of this SAG system, this research also proposed some recommendations such as holistic planning, science-technology innovation, resource sharing, multi-stakeholders participation, and expansion of application fields, so as to drive this idea to the reality. Reference | Related Articles | Metrics | Comments（0）

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Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle YANG Guofeng, HE Yong, FENG Xuping, LI Xiyao, ZHANG Jinnuo, YU Zeyu Smart Agriculture    2022, 4 (1): 1-16.   DOI: 10.12133/j.smartag.SA202201008 Abstract （2737）   HTML （726）    PDF（pc） （937KB）（2233）       Knowledge map    Save Diseases and pests are main stresses to crop production. It is necessary to accurately and quickly monitor and control the stresses dynamically, so as to ensure the food security and the quality and safety of agricultural products, protect the ecological environment, and promote the sustainable development of agriculture. In recent years, with the rapid development of the unmanned aerial vehicle (UAV) industry, UAV agricultural remote sensing has played an important role in the application of crop diseases and pests monitoring due to its high image spatial resolution, strong data acquisition timeliness and low cost. The relevant background of UAV remote sensing monitoring of crop disease and pest stress was introduced, then the current methods commonly used in remote sensing monitoring of crop disease and pest stress by UAV was summarized. The data acquisition method and data processing method of UAV remote sensing monitoring of crop disease and pest stress were mainly discussed. Then, from the six aspects of visible light imaging remote sensing, multispectral imaging remote sensing, hyperspectral imaging remote sensing, thermal infrared imaging remote sensing, LiDAR imaging remote sensing and multiple remote sensing fusion and comparison, the research progress of remote sensing monitoring of crop diseases and pests by UAV worldwide was reviewed. Finally, the unresolved key technical problems and future development directions in the research and application of UAV remote sensing monitoring of crop disease and pest stress were proposed. Such as, the performance of the UAV flight platform needs to be optimized and upgraded, as well as the development of low-cost, lightweight, modular, and more adaptable airborne sensors. Convenient and automated remote sensing monitoring tasks need to be designed and implemented, and more remote sensing monitoring information can be obtained. Data processing algorithms or software should be designed and developed with greater applicability and wider applicability, and data processing time should be shortened by using 5G-based communication networks and edge computing devices. The applicability of the algorithm or model for UAV remote sensing monitoring of crop disease and pest stress needs to be stronger, so as to build a corresponding method library. We hope that this paper can help Chinese UAV remote sensing monitoring of crop diseases and pests to achieve more standardization, informatization, precision and intelligence. Reference | Related Articles | Metrics | Comments（0）

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Indoor phenotyping platforms and associated trait measurement: Progress and prospects Xu Lingxiang, Chen Jiawei, Ding Guohui, Lu Wei, Ding Yanfeng, Zhu Yan, Zhou Ji Smart Agriculture    2020, 2 (1): 23-42.   DOI: 10.12133/j.smartag.2020.2.1.202003-SA002 Abstract （2619）   HTML （5484）    PDF（pc） （1588KB）（2225）       Knowledge map    Save Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perspectivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorized according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future. 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 （1064）   HTML （116）    PDF（pc） （3336KB）（2132）       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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Research and prospect of solar insecticidal lamps Internet of Things Li Kailiang, Shu Lei, Huang Kai, Sun Yuanhao, Yang Fan, Zhang Yu, Huo Zhiqiang, Wang Yanfei, Wang Xinyi, Lu Qiaoling, Zhang Yacheng Smart Agriculture    2019, 1 (3): 13-28.   DOI: 10.12133/j.smartag.2019.1.3.201905-SA001 Abstract （2516）   HTML （776）    PDF（pc） （3195KB）（2060）       Knowledge map    Save Along with the increasing awareness of environmental protection and growing demand for green and pollution-free agricultural products, it has a great need to explore new ways to apply greener pest control methods in agricultural production. Researching on Solar Insecticidal Lamps (SILs) has continuously received incremental attentions from both the academia and industry, which brings a new mode for the preventing and controlling of agricultural migratory pests with phototaxis feature, and now is becoming to a hot research topic. Towards the fast development of "precision agriculture" and "smart agriculture" as well as the increasing demands for agricultural informatization, Wireless Sensor Networks (WSNs) have been widely used for agricultural information collection and intelligent control of agricultural equipment. WSNs are suitable for large-scale deployment and regional monitoring, which can be easily combined with SIL nodes. Based on the combination, a new type of agricultural Internet of things - Solar Insecticidal Lamps Internet of Things (SIL-IoTs) was proposed and the technology of WSNs for the prevention and control of phototactic migratory pests in agricultural applications were surveyed. Firstly, the state-of-art insecticidal lamps applications was reviewed and their characteristics deployment manners and working lifetime in the production of crops (e.g., forest, fruits, rice, vegetables) were summarized. Secondly, the characteristics of existing GSM/3G/4G-enabled SIL nodes and their latest research status on SIL-IoTs were summarized. Furthermore, the research status was analyzed concerning the energy harvesting mode and deployment characteristics of SIL, which are solar energy SIL harvesting mode for energy saving and the heuristic mode for node deployment, respectively. Finally, towards the fast-developed vision of smart agriculture, in which various emerging IT and automation technologies are maturely applied, SIL-IoTs can be considered as a new and important component to contribute to the green agricultural pest monitoring and control. To further enhance SIL-IoTs' capability and enrich SIL-IoTs' function, four open research issues on SIL-IoTs were proposed, i.e., 1) optimized deployment scheme of SIL-IoTs with multiple constrains, 2) optimized and adaptive energy management strategy for ensuring normal working hours of SIL node, 3) lack of algorithms for pests outbreak area localization, and 4) interference on data transmission because of dense high voltage discharge during severe pest disaster. To sum up, SIL-IoTs is one of the representative applications of "precision agriculture" and "smart agriculture" based on WSNs, which is a new model on prevention and control of pests. The combination of both optimized deployment algorithms of SIL-IoTs nodes and artificial intelligence techniques will provide a theoretical basis for SIL-based applications in terms of optimized deployment and energy management. Intelligent pest information collection, alarm, and node' senergy management via SIL-IoTs will facilitate decisions-makings for precise agricultural applications in prevention and control of pests. 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 （499）   HTML （68）    PDF（pc） （2214KB）（2055）       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 （863）   HTML （111）    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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Design and application of data acquisition and analysis system for CropSense Wang Jiaojiao, Xu Bo, Wang Congcong, Yang Guijun, Yang Zhong, Mei Xin, Yang Xiaodong Smart Agriculture    2019, 1 (4): 91-104.   DOI: 10.12133/j.smartag.2019.1.4.201910-SA002 Abstract （1341）   HTML （1793）    PDF（pc） （1549KB）（2025）       Knowledge map    Save In view of the demand of small and medium-sized farms for rapid monitoring and accurate diagnosis of crop growth, the National Engineering Research Center for Information Technology in Agriculture (NERCITA) designed a crop growth monitoring device which named CropSense. It is a portable crop health analysis instrument based on dual-channel high-throughput spectral signals which derived from the incident and reflected light intensity of the crop canopy at red and near-infrared bands. This paper designed and implemented a data collecting and analyzing system for CropSense. It consisted of a mobile application for collecting data of CropSense and a server-side system for data and model management. The system implemented data collecting, processing, analyzing and management completely. The system calculated normalized differential vegetation index (NDVI) based on the two-channels spectral sampling data from CropSense which connected smart phone by Bluetooth, then generated crop growth parameters about nitrogen content, chlorophyll content and Leaf Area Index with the built-in spectral inversion model in the server. Meanwhile, it calculated vegetation coverage, density and color content by images captured from the camera of smart phone. When we finished the sampling program, it generated growth parameter thematic maps by Kriging interpolation based on all sampling data of the selected fields. Considering the target yield of the plot, it could provide expert advice visually. Users could get diagnostic information and professional guiding scheme of crop plots immediately after collecting data by touch a button. Now the device and system have been applied in some experimental farms of research institutes. This paper detailed application of the system in XiaoTangShan farm of NERCITA. Compared with the traditional corn flare period samples and fertilize schemes, users could avoid errors caused by manual recording. Besides, with the same corn yield, the fertilization amount has reduced 16.67% when using the generation of the variable fertilization scheme by this system. The result showed that the system could get the crop growth status efficiently and produced reasonable fertilization. The system collected and analyzed crop growth efficiently and conveniently. It is suitable for various farmers without expertise to obtain the information of the crop growth timely and can guide them to operate more effectively and economically in the field. The system saved data to web server through the Internet which improved the shortcoming of poor sharing in the traditional data exporting mode. This system is practical and promising, and it will be widely applied in the explosion of family farms in China. Reference | Related Articles | Metrics | Comments（0）

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Development and performance evaluation of a multi-rotor unmanned aircraft system for agricultural monitoring Zhu Jiangpeng, Cen Haiyan, He Liwen, He Yong Smart Agriculture    2019, 1 (1): 43-52.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA011 Abstract （1658）   HTML （668）    PDF（pc） （1255KB）（2019）       Knowledge map    Save In modern agriculture production, to obtain real-time, accurate and comprehensive information of the farmlands is necessary for farmers. Unmanned Aircraft System (UAS) is one of the most popular platforms for agricultural information monitoring, especially the multi-rotor aircraft due to its simplicity of operation. It is easy to control the speed and altitude of multi-rotor aircraft, even at low altitude. The above features enable multi-rotor UAS to acquire high-resolution images at low altitudes by integrating different imaging sensors. The aim of this work was to develop an octocopter UAS for agricultural information monitoring. In order to obtain the high-resolution aerial images of the entire experimental field, the Sony Nex-7 camera was attached to the aircraft. According to the real-time position of the aircraft got from global position system (GPS) and inertial measurement unit (IMU), the flight control system of the aircraft will send signals to control the camera to capture images at desired locations. Besides, position and orientation system (POS) and an illuminance sensor were loaded on the aircraft to get the location, shooting angle and ambient illumination information of each image. The system can be used to collect the remote sensing data of a field, and the performance was comprehensively evaluated in the field of oilseed rape experimental station in Zhuji, Zhejiang Province, China. The result shows that the system can keep the camera optical axis perpendicular to the ground during the operation. Because the effective communication was established between the mission equipment and the flight control system, the UAS can accurately acquire the images at the pre-defined locations, which improved the operation efficiency of the system. The images collected by the system can be mosaicked into an image of the whole field. In summary, the system can satisfy the demand for the agricultural information collection. Reference | Related Articles | Metrics | Comments（0）

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Development of precision service system for intelligent agriculture field crop production based on BeiDou system Wu Caicong, Fang Xiangming Smart Agriculture    2019, 1 (4): 83-90.   DOI: 10.12133/j.smartag.2019.1.4.201911-SA001 Abstract （1612）   HTML （1129）    PDF（pc） （1195KB）（1941）       Knowledge map    Save Precision navigation technology of agricultural machinery is being applied on a large scale for field crop production in China. The technology can reduce labor cost, improve working quality, and extend working time. However, the precision application technology of agricultural machinery and precision management technology of agricultural production are still slow in development. The technology, equipment, and service system of precision agriculture have not been completely developed yet in China. There is still a lack of scientific and technical means to achieve the main objectives of cost saving, efficiency improvement, energy saving, and environmental protection in crop production. With the integration of material, energy, and information, intelligent agricultural machinery system is being developed to provide a safer, more efficient, and more scientific solution for agricultural production. In view of the characteristics of intelligent agricultural machinery system, the characteristics of socialized service of agricultural machinery in China, and the status quo of agricultural financial subsidies, this paper puts forward an idea that to develop a socialized precision service system of agricultural machinery, in order to achieve cost saving, efficiency improvement, energy saving, and environmental protection for crop production. The system includes the core participants in agricultural machinery production operations, such as agricultural production organizations, agricultural machinery service organizations, related agriculture management authorities, and the third-party data management service organization. The key technologies for the system include the intelligent gateway technology of agricultural machinery, the variable controlling and measurement technology of fertilizer and chemical, the big data management service technology, and the technology of professional application service platform. During the field operation, the agricultural machinery can control the application of fertilizer or chemical according the prescription map and send the data of position and flow to the database belongs to the third-party organization designated by the government. Therefore, the construction of this system can be used as a basis for the social services and the granting of subsidies. The government can set related standards of application of fertilizer or chemical, and pay the subsidies for the machinery operation according to the operating area when the farmers achieve the standards, which may encourage the farmers to adopt the advanced technology to save fertilizer and chemical. The study provides solutions and technical means to achieve the goal of reducing both fertilizer and chemicals, to adjust of the state’s relevant agricultural subsidy policies, and to promote the comprehensive application of China’s precision agricultural technology. Reference | Related Articles | Metrics | Comments（0）

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Development and Application of an Intelligent Remote Management Platform for Agricultural Machinery ZHU Dengsheng​, FANG Hui​, HU Shaoming​, WANG Wenquan​, ZHOU Yansuo​, WANG Hongyan​, LIU Fei​, HE Yong​ Smart Agriculture    2020, 2 (2): 67-81.   DOI: 10.12133/j.smartag.2020.2.2.202004-SA006 Abstract （1538）   HTML （1204）    PDF（pc） （2701KB）（1932）       Knowledge map    Save In order to solve problems such as the lack of real-time data in agricultural machinery management, the difficulty in real-time machine operation supervision and the asymmetry of machine service information, an intelligent remote management platform was developed in this research. Firstly, five design principles of a specialized remote agricultural machinery management system: specialization, standardization, cloud platform, modularity and openness were proposed. Based on these principles, a customizable general-purpose intelligent remote management system for agricultural machinery based on intelligent sensing technology, Internet of Things technology, positioning technology, remote sensing technology and geographic information system was designed. Practical modules, including agricultural machinery information-based and location-based services using WebGIS, real-time monitoring and management of machinery operation, basic information management of farmland, basic information management of crops in the field, dispatching management of machinery, subsidy management of machinery, order management of machinery operation were designed and implemented in the platform for users of government agencies, agricultural machinery corporations, machine operators, and farmers. Besides, some key technologies of the platform under the current technical background, including the calculation method of the working area with low-precision GNSS positioning receivers, the analysis of anomality data during the processing of GNSS positioning data, the machine scheduling algorithm development, the integration of sensors were focused， analyzed and implementd. The idea of building the machinery management platform with each individual field as the building block was developed. It can be predicted that the agricultural machinery operation management platform would gradually change from simple operation management to field-level comprehensive management. The research and development of this platform can not only solve current machinery management problems, but also provide basic functions for development of similar machinery management platforms. Reference | Related Articles | Metrics | Comments（0）

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Research on key technologies of crop growth process simulation model and morphological 3D visualization Zhu Yeping, Li Shijuan, Li Shuqin Smart Agriculture    2019, 1 (1): 53-66.   DOI: 10.12133/j.smartag.2019.1.1.201901-SA005 Abstract （2073）   HTML （1720）    PDF（pc） （2416KB）（1889）       Knowledge map    Save According to the demand of digitized analysis and visualization representation of crop yield formation and variety adaptability analysis, aiming at improving the timeliness, coordination and sense of reality of crop simulation model, key technologies of crop growth process simulation model and morphological 3D visualization were studied in this research. The internet of things technology was applied to collect the field data. The multi-agent technology was used to study the co-simulation method and design crop model framework. Winter wheat (Triticum aestivum L.) was taken as an example to conducted filed test, the 3D morphology visualization system was developed and validated. Taking three wheat varieties, Hengguan35 (Hg35), Jimai22 (Jm22) and Heng4399 (H4399) as research objects, logistic equation was constructed to simulate the change of leaf length, maximum leaf width, leaf height and plant height. Parametric modeling method and 3D graphics library (OpenGL) were used to build wheat organ geometry model so as to draw wheat morphological structure model. The R2 values of leaf length, maximum leaf width, leaf height and plant height were between 0.772-0.999, indicating that the model has high fitting degree. F values (between 10.153-4359.236) of regression equation and Sig. values (under 0.05) show that the model has good significance. Taking wheat as example, this research combined wheat growth model and structure model effectively in order to realize the 3D morphology visualization of crop growth processes under different conditions, it will provide references for developing the crop simulation visualization system, the method and related technologies are suitable for other field crops such as corn and rice, etc. Reference | Related Articles | Metrics | Comments（0）

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Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology ZHANG Jian, XIE Tianjin, YANG Wanneng, ZHOU Guangsheng Smart Agriculture    2021, 3 (1): 1-15.   DOI: 10.12133/j.smartag.2021.3.1.202102-SA033 Abstract （2958）   HTML （475）    PDF（pc） （1983KB）（1886）       Knowledge map    Save Plant height is a key indicator to dynamically measure crop health and overall growth status, which is widely used to estimate the biological yield and final grain yield of crops. The traditional manual measurement method is subjective, inefficient, and time-consuming. And the plant height obtained by sampling cannot evaluate the height of the whole field. In the last decade, remote sensing technology has developed rapidly in agriculture, which makes it possible to collect crop height information with high accuracy, high frequency, and high efficiency. This paper firstly reviewed the literature on obtaining plant height by using remote sensing technology for understanding the research progress of height estimation in the field. Unmanned aerial vehicle (UAV) platform with visible-light camera and light detection and ranging (LiDAR) were the most frequently used methods. And main research crops included wheat, corn, rice, and other staple food crops. Moreover, crop height measurement was mainly based on near-field remote sensing platforms such as ground, UAV, and airborne. Secondly, the basic principles, advantages, and limitations of different platforms and sensors for obtaining plant height were analyzed. The altimetry process and the key techniques of LiDAR and visible-light camera were discussed emphatically, which included extraction of crop canopy and soil elevation information, and feature matching of the imaging method. Then, the applications using plant height data, including the inversion of biomass, lodging identification, yield prediction, and breeding of crops were summarized. However, the commonly used empirical model has some problems such large measured data, unclear physical significance, and poor universality. Finally, the problems and challenges of near-field remote sensing technology in plant height acquisition were proposed. Selecting appropriate data to meet the needs of cost and accuracy, improving the measurement accuracy, and matching the plant height estimation of remote sensing with the agricultural application need to be considered. In addition, we prospected the future development was prospected from four aspects of 1) platform and sensor, 2) bare soil detection and interpolation algorithm, 3) plant height application research, and 4) the measurement difference of plant height between agronomy and remote sensing, which can provide references for future research and method application of near-field remote sensing height measurement. Reference | Related Articles | Metrics | Comments（0）

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Framework design and application prospect of agricultural product information blockchain Liang Hao, Liu Sichen, Zhang Yinuo, Lv Ke Smart Agriculture    2019, 1 (1): 67-75.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA020 Abstract （1910）   HTML （1927）    PDF（pc） （1064KB）（1884）       Knowledge map    Save Agriculture of China is a typical agricultural system of small producer and large market. Producers are too scattered. Agricultural foundation remains weak in the vast rural areas, especially poverty-stricken areas. All of the above have built up a hedge for the high input of information industry and been a serious impediment to agricultural informationalization. As the underlying technology of electronic tokens, blockchain has become the current research hotspot, and been applied in finance, logistics, electronic commerce, information traceability and so on. Blockchain is a distributed storage and computation system, which is decentralized, so it is highly compatible with the distributed economic system. It will be a comprehensive solution to the agricultural status that is "scattered, small and weak". The book China Blockchain Technology and Application Development Whitepaper 2016 gives guidance suggestions for the application of blockchain in different industries including agriculture. This article is proposed by combining the specific status of Chinese agriculture and the technical characteristics of the block chain. Blockchain will play a very important rule in information gathering, resource integrating, profits sharing and backtracking information. Framework of blockchain for agricultural product with 7 levels of information gathering layer, data layer, network layer, consensus layer, excitation layer, contract layer and application layer was designed based on generalized blockchain according to actual situation of agriculture in China. The function of information gathering layer and data layer weve used to storage encrypted information that is acquired by IoT node in a distributed way. The Network layer was designed with a semi-distributed topological structure based on the original Peer-to-Peer distributed network structure in the blockchain by adding the supper notes. In the consensus layer, the DPOS was implied install of POW, therefore, there is no need for intensive computing. Due to the use of smart contact in the contract layer, transactions can be completed automatically in the absence of intermediaries. Furthermore, members of the Blockchain Union can also get repay by participating in the consensus with the smart contracts. The application layer was designed to provide the interface for application of government, bank, enterprise, producer and consumers. This framework can provide flexible mechanism of distributed storage, complete information consensus system, reliable information tamper-proof function and practical incentive reward measures. Subsequently, the above functions have been explained in the agricultural products quality safety traceability and application for the agricultural product market information transparency in more detail. However, the application of block chain in the field of agriculture is still in the stage of exploration, the technology is far from mature, and still need to be perfected in the process of application gradually. 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 （978）   HTML （138）    PDF（pc） （1435KB）（1726）       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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Research Progress of Sensing Detection and Monitoring Technology for Fruit and Vegetable Quality Control GUO Zhiming, WANG Junyi, SONG Ye, ZOU Xiaobo, CAI Jianrong Smart Agriculture    2021, 3 (4): 14-28.   DOI: 10.12133/j.smartag.2021.3.4.202106-SA011 Abstract （1377）   HTML （168）    PDF（pc） （959KB）（1720）       Knowledge map    Save Vegetable and fruit planting areas and products of China have always ranked first in the world, and the vegetable and fruit industry is respectively the second and third largest agricultural planting industry after grain. Vegetables and fruits are prone to quality deterioration during postharvest storage and transportation, resulting in reduced edible value and huge economic losses. To ensure fruit and vegetable quality and reduce the waste of resources caused by postnatal deterioration, this paper summarizes the latest research status of sensor detection and monitoring technology for fruit and vegetable quality deterioration and analyzed the principle, characteristics, advantages, and disadvantages of various detection technology. Among them, machine vision can detect the external quality and surface defects of fruits and vegetables, but fruits and vegetables are different from the standard machined products, and they are affected by many factors in the growth process, which seriously interfere with the image collection work and easily lead to misjudgment. An electronic nose equipped with expensive gas sensors can monitor the odor deterioration of fruits and vegetables but would require improved sensitivity and durability. Near-infrared can detect the internal quality and recessive defects of fruits and vegetables, but the applicability of the model needs to be improved. Hyperspectral imaging can visually detect the internal and external quality of fruits and vegetables and track the deterioration process, but the huge amount of data obtained leads to data redundancy, which puts forward higher requirements for system hardware. Therefore, low-cost multispectral imaging systems should be developed and characteristic wavelength extraction algorithms should be optimized. Raman spectroscopy can detect fruit and vegetable spoilage bacteria and their metabolites, but there is no effective Raman enhanced substrate production and accurate Raman standard spectrogram database. The comprehensive evaluation of fruit and vegetable deterioration can be realized by multi-technology and multi-information fusion. It can overcome the limitation of single sensor information analysis, improve the robustness and parallel processing ability of the detection model, and provide a new approach for high-precision detection or monitoring of fruit and vegetable quality deterioration. The Internet of Things monitoring system is constructed with various sensors as the sensing nodes to realize the intelligent real-time monitoring of fruit and vegetable quality deterioration information, provide a reference for solving the technical limitation of quality deterioration control in the processing of fruit and vegetable. This is of great significance for reducing the postpartum economic loss of fruits and vegetables and promoting the sustainable development of the fruit and vegetable industry. Reference | Related Articles | Metrics | Comments（0）

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Original innovation of key technologies leading healthy development of smart agricultural Gao Wanlin, Zhang Ganghong, Zhang Guofeng, Huang Feng, Wu Dehua, Tao Sha, Wang Minjuan Smart Agriculture    2019, 1 (1): 8-19.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA015 Abstract （2420）   HTML （2148）    PDF（pc） （824KB）（1707）       Knowledge map    Save Smart agricultural is a new form of agriculture that makes full use of human wisdom to develop agriculture. It is a new stage, new model and new pattern of agricultural development. The development of agricultural information technology is an inevitable requirement for smart agricultural. The new generation of core information technology, such as agricultural big data, cloud computing, Internet of things, artificial intelligence, can enable the innovative development of smart agricultural. It can provide new technologies, new methods and new solutions for the healthy development of smart agricultural. Agricultural informationization standardization is the premise to guide the progress and innovation of agricultural science and technology. It can lead the progress of agricultural science and technology and standardize the process of agricultural production. It is an urgent need for the development of smart agricultural. Agricultural Internet of things and agricultural application-specific chip are the core technologies and equipment for the development of smart agricultural. The application demand of agricultural Internet of things can promote the development of agricultural application-specific chip technology. The technological innovation of agricultural application-specific chip will promote the technological upgrading of agricultural Internet of things. Agricultural big data and cloud computing are powerful technical support for massive and complex agricultural information processing. The computing requirements of big data algorithms can promote the innovation and development of cloud computing technology. The improvement of cloud computing capability is more convenient for the application of big data algorithms and applications. Agricultural information security and blockchain are the key to guarantee the security of agricultural information, agricultural product quality certification system and agricultural. Agricultural artificial intelligence is the inevitable choice to improve agricultural labor productivity, reduce resource consumption, and efficient production. The innovation and application of artificial intelligence algorithm is an effective measure to realize smart agricultural. Agricultural plasma technology provides a new technological means for smart agricultural to produce more safer and more reassuring green organic agricultural products. It can be used in different stages of agricultural production, includes before, during and after production, to protect the healthy development of the whole agricultural production chain. The original innovation and autonomous control of the key technologies of smart agricultural will surely lead the healthy development of smart agricultural. Reference | Related Articles | Metrics | Comments（0）

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Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing HAN Dong, WANG Pengxin, ZHANG Yue, TIAN Huiren, ZHOU Xijia Smart Agriculture    2021, 3 (2): 1-14.   DOI: 10.12133/j.smartag.2021.3.2.202104-SA002 Abstract （1613）   HTML （231）    PDF（pc） （1255KB）（1680）       Knowledge map    Save Agricultural drought is a major factor that affects agricultural production. Traditional agricultural drought monitoring is mainly based on meteorological and hydrological data, and although it can provide more accurate drought monitoring results at the point level, there are still limitations in monitoring agricultural drought at the regional scale. The rapid development of remote sensing technology has provided a new mean of monitoring agricultural droughts at the regional scale, especially since the electromagnetic wavelengths sensed by satellite sensors in orbit now cover visible, near-infrared, thermal infrared and microwave wavelengths. It is important to make full use of the rich surface information obtained from satellite remote sensing data for agricultural drought monitoring and forecasting. This paper described the research progress of agricultural drought monitoring based on satellite remote sensing from three aspects: remote sensing index-based method, soil water content method and crop water demand method. The research progress of agricultural drought monitoring based on remote sensing index-based method was elaborated from five aspects: vegetation drought index, temperature drought index, integrated vegetation and temperature drought index, water drought index and microwave drought index; the research progress of agricultural drought monitoring based on soil water content method was elaborated from two aspects: soil water content retrieval based on visible to thermal infrared data and soil water content retrieval based on microwave data; the research progress of agricultural drought monitoring based on crop water demand method was elaborated from two aspects: agricultural drought monitoring based on crop canopy water content retrieval method and crop growth model method. Agricultural drought forecasting is a timeline prediction based on drought monitoring. Based on the summary of the progress of drought monitoring, the research progress of agricultural drought forecasting by the drought index method and the crop growth model method was further briefly described. The existing agricultural drought monitoring methods based on satellite remote sensing were summarized, and its shortcomings were sorted out, and some prospects were put forward. In the future, different remote sensing data sources can be used to combine deep learning methods with crop growth models and based on data assimilation methods to further explore the potential of satellite remote sensing data in the monitoring of agricultural drought dynamics, which can further promote the development of smart agriculture. Reference | Related Articles | Metrics | Comments（0）

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Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5 LI Zhijun, YANG Shenghui, SHI Deshuai, LIU Xingxing, ZHENG Yongjun Smart Agriculture    2021, 3 (2): 100-114.   DOI: 10.12133/j.smartag.2021.3.2.202105-SA005 Abstract （1785）   HTML （126）    PDF（pc） （3571KB）（1674）       Knowledge map    Save Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved YOLOv5 fruit detection network and a yield fitting network was proposed. The in-situ images of the apples without bags at different periods were acquired by using an unmanned aerial vehicle and Raspberry Pi camera, formed an image sample data set. For dealing with no attention preference and the parameter redundancy in feature extraction, the YOLOv5 network was improved by two approaches: 1) replacing the depth separable convolution, and 2) adding the attention mechanism module, so that the computation cost was decreased. Based on the improvement, the quantity of fruit was estimated and the total area of the bounding box of apples were respectively obtained as output. Then, these results were used as the input of the yield fitting network and actual yields were applied as the output to train the yield fitting network. The final model of fruit tree production estimation was obtained by combining the improved YOLOv5 network and the yield fitting network. Yield estimation experimental results showed that the improved YOLOv5 fruit detection algorithm could improve the recognition accuracy and the degree of lightweight. Compared with the previous algorithm, the detection speed of the algorithm proposed in this research was increased by up to 15.37%, while the mean of average accuracy (mAP) was raised up to 96.79%. The test results based on different data sets showed that the lighting conditions, coloring time and with white cloth in background had a certain impact on the accuracy of the algorithm. In addition, the yield fitting network performed better on predicting the yield of apple trees. The coefficients of determination in the training set and test set were respectively 0.7967 and 0.7982. The prediction accuracy of different yield samples was generally stable. Meanwhile, in terms of the with/without of white cloth in background, the range of relative error of the fruit tree yield measurement model was respectively within 7% and 13%. The yield estimation method of apple tree based on improved lightweight YOLOv5 had good accuracy and effectiveness, which could achieve yield estimation of apples in the natural environment, and would provide a technical reference for intelligent agricultural equipment in modern orchard environment. Reference | Related Articles | Metrics | Comments（0）

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Research Advances and Prospects of Crop 3D Reconstruction Technology ZHU Rongsheng, LI Shuai, SUN Yongzhe, CAO Yangyang, SUN Kai, GUO Yixin, JIANG Bofeng, WANG Xueying, LI Yang, ZHANG Zhanguo, XIN Dawei, HU Zhenbang, CHEN Qingshan Smart Agriculture    2021, 3 (3): 94-115.   DOI: 10.12133/j.smartag.2021.3.3.202102-SA002 Abstract （1731）   HTML （251）    PDF（pc） （1950KB）（1656）       Knowledge map    Save Crop 3-dimensional (3D) reconstruction is one of the most fundamental techniques in crop phenomics, and is an important tool to accurately describe the holographic structure of crop morphology. 3D reconstruction models of crops are important for high-throughput crop phenotype acquisition, crop plant characteristics evaluation, and plant structure and phenotype correlation analysis. In order to promote and popularize the 3D reconstruction technology in crop phenotype research, the basic methods and application characteristics, the current advances of research and the prospects of 3D reconstruction in crops were review in this paper. Firstly, the existing methods of crop 3D reconstruction were summarized, the basic principles of each method were reviewed, the characteristics, advantages and disadvantages of each method were analyzed, the applicability of each method on the basis of the general process of crop 3D reconstruction methods were introduced, and the specific process and considerations for the implementation of each method were summarized. Secondly, the application of crop 3D reconstruction were divided into three parts: single crop reconstruction, field group reconstruction and root system, according to different target objects, and the applications of crop 3D reconstruction technology from these three perspectives were reviewed, the research advances of each method for different crop 3D reconstruction based on accuracy, speed and cost were explored, and the problems and challenges of crop 3D reconstruction in the context of different reconstruction objects were organized. Finally, the prospects of crop 3D reconstruction technology were analyzed. 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）（1638）       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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Distinguishing Volunteer Corn from Soybean at Seedling Stage Using Images and Machine Learning FLORES Paulo, ZHANG Zhao, MATHEW Jithin, JAHAN Nusrat, STENGER John Smart Agriculture    2020, 2 (3): 61-74.   DOI: 10.12133/j.smartag.2020.2.3.202007-SA002 Abstract （1818）   HTML （2685）    PDF（pc） （1967KB）（1593）       Knowledge map    Save Volunteer corn in soybean fields are harmful as they disrupt the benefits of corn-soybean rotation. Volunteer corn does not only reduce soybean yield by competing for water, nutrition and sunlight, but also interferes with pest control (e.g., corn rootworm). It is therefore critical to monitor the volunteer corn in soybean at the crop seedling stage for better management. The current visual monitoring method is subjective and inefficient. Technology progress in sensing and automation provides a potential solution towards the automatic detection of volunteer corn from soybean. In this study, corn and soybean were planted in pots in greenhouse to mimic field conditions. Color images were collected by using a low-cost Intel RealSense camera for five successive days after the germination. Individual crops from images were manually cropped and subjected to image segmentation based on color threshold coupled with noise removal to create a dataset. Shape (i.e., area, aspect ratio, rectangularity, circularity, and eccentricity), color (i.e., R, G, B, H, S, V, L, a, b, Y, Cb, and Cr) and texture (coarseness, contrast, linelikeness, and directionality) features of individual crops were extracted. Individual feature's weights were ranked with the top 12 relevant features selected for this study. The 12 features were fed into three feature-based machine learning algorithms: support vector machine (SVM), neural network (NN) and random forest (RF) for model training. Prediction precision values on the test dataset for SVM, NN and RF were 85.3%, 81.6%, and 82.0%, respectively. The dataset (without feature extraction) was fed into two deep learning algorithms—GoogLeNet and VGG-16, resulting into 96.0% and 96.2% accuracies, respectively. The more satisfactory models from feature-based machine learning and deep learning were compared. VGG-16 was recommended for the purpose of distinguishing volunteer corn from soybean due to its higher detection accuracy, as well as smaller standard deviation (STD). This research demonstrated RGB images, coupled with VGG-16 algorithm could be used as a novel, reliable (accuracy >96%), and simple tool to detect volunteer corn from soybean. The research outcome helps provide critical information for farmers, agronomists, and plant scientists in monitoring volunteer corn infestation conditions in soybean for better decision making and management. Reference | Related Articles | Metrics | Comments（0）

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Identification and Morphological Analysis of Adult Spodoptera Frugiperda and Its Close Related Species Using Deep Learning WEI Jing, WANG Yuting, YUAN Huizhu, ZHANG Menglei, WANG Zhenying Smart Agriculture    2020, 2 (3): 75-85.   DOI: 10.12133/j.smartag.2020.2.3.202008-SA001 Abstract （1775）   HTML （851）    PDF（pc） （1962KB）（1566）       Knowledge map    Save Invasive pest fall armyworm (FAW) Spodoptera frugiperda is one of the serious threats to the food safety. Early warning and control plays a key role in FAW management. Nowadays, deep learning technology has been applied to recognize the image of FAW. However, there is a serious lack of training dataset in the current researches, which may mislead the model to learn features unrelated to the key visual characteristics (ring pattern, reniform pattern, etc.) of FAW adults and its close related species. Therefore, this research established a database of 10,177 images belonging to 7 species of noctuid adults, including FAW and 6 FAW close related species. Based on the small-scale dataset, transfer learning was used to build the recognition model of FAW adults by employing three deep learning models (VGG-16, ResNet-50 and DenseNet-121) pretrained on ImageNet. All of the models got more than 98% recognition accuracy on the same testing dataset. Moreover, by using feature visualization techniques, this research visualized the features learned by deep learning models and compared them to the related key visual characteristics recognized by human experts. The results showed that there was a high consistency between the two counterparts, i.e., the average feature recognition rate of ResNet-50 and DenseNet-121 was around 85%, which further demonstrated that it was possible to use the deep learning technology for the real-time monitoring of FAW adults. In addition, this study also found that the learning abilities of key visual characteristics among different models were different even though they have similar recognition accuracy. Herein, we suggest that when evaluating the model capacity, we should not only focus on the recognition rate, the ability of learning individual visual characteristics should be allocated importance for evaluating the model performance. For those important taxonomical traits, if the visualization results indicated that the model didn't learnt them, we should then modify our datasets or adjusting the training strategies to increase the learning ability. In conclusion, this study verified that visualizing the features learnt by the model is a good way to evaluate the learning ability of deep learning models, and to provide a possible way for other researchers in the field who want to understand the features learnt by deep learning models. 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 （1540）   HTML （422）    PDF（pc） （2824KB）（1548）       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 （1417）   HTML （202）    PDF（pc） （853KB）（1523）       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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Development of an automatic steering test bench for tractors Du Juan, Li Min, Jin Chengqian, Yin Xiang Smart Agriculture    2019, 1 (2): 85-93.   DOI: 10.12133/j.smartag.2019.1.2.201903-SA002 Abstract （1110）   HTML （334）    PDF（pc） （1055KB）（1467）       Knowledge map    Save In recent years, the technology of automatic tractor navigation based on satellite positioning has greatly improved the efficiency and accuracy of tractor field work. Automatic steering contributes is one of the key technologies to realize the automation and intelligentization of agricultural mechanization. It costs much time to install and test automatic steering systems for tractors in the field due to complicated conditions. An automatic steering test bench was developed to reduce time consumed in the field by conducting simulation tests on accuracy and reliability. The developed automatic steering system can be applied to the tractor after obtaining satisfactory results on the test bench, which will greatly shorten the development cycle and improve the precision of the system. In this study, a 120-horsepower tractor front axle was selected. Through the design and calculation of mechanical structure, hydraulic system and electrical control system, the tractor automatic steering test bench was built. The mechanical body consist of a tractor front axle assembly, a loading device and a mechanical frame. The hydraulic interface was reserved for post-installation of automatic steering devices. Two inertial measurement units were used to test the steering system performance by recording the rotation angle of front wheels and the steering wheel. The steering wheel had a steering clearance of 16.48° and an average wheel delay time of 0.14s. In the general range and small angle range, there is a small deviation between the actual corner and the theoretical corner. Responsibility and stability met requirements for agricultural machinery steering. Experimental results show that the test bench has stable performance in terms of status detection, steering control and measurement analysis, which could meet requirements for verifying working parameters of automatic steering devices. The research provides an efficient and reliable test bench for commissioning and performance testing of agricultural machinery automatic steering. 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 （790）   HTML （136）    PDF（pc） （1118KB）（1442）       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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Apple detection model based on lightweight anchor-free deep convolutional neural network Xia Xue, Sun Qixin, Shi Xiao, Chai Xiujuan Smart Agriculture    2020, 2 (1): 99-110.   DOI: 10.12133/j.smartag.2020.2.1.202001-SA004 Abstract （1914）   HTML （1797）    PDF（pc） （2005KB）（1411）       Knowledge map    Save Intelligent production and robotic oporation are the efficient and sustainable agronomic route to cut down economic and environmental costs and boosting orchard productivity. In the actual scene of the orchard, high performance visual perception system is the premise and key for accurate and reliable operation of the automatic cultivation platform. Most of the existing apple detection models, however, are difficult to be used on the platforms with limited hardware resources in terms of computing power and storage capacity due to too many parameters and large model volume. In order to improve the performance and adaptability of the existing apple detection model under the condition of limited hardware resources, while maintaining detection accuracy, reducing the calculation of the model and the model computing and storage footprint, shorten detection time, this method improved the lightweight MobileNetV3 and combined the object detection network which was based on keypoint prediction (CenterNet) to build a lightweight anchor-free model (M-CenterNet) for apple detection. The proposed model used heatmap to search the center point (keypotint) of the object, and predict whether each pixel was the center point of the apple, and the local offset of the keypoint and object size of the apple were estimated based on the extracted center point without the need for grouping or Non-Maximum Suppression (NMS). In view of its advantages in model volume and speed, improved MobileNetV3 which was equipped with transposed convolutional layers for the better semantic information and location information was used as the backbone of the network. Compared with CenterNet and SSD (Single Shot Multibox Detector), the comprehensive performance, detection accuracy, model capacity and running speed of the model were compared. The results showed that the average precision, error rate and miss rate of the proposed model were 88.9%, 10.9% and 5.8%, respectively, and its model volume and frame rate were 14.2MB and 8.1fps. The proposed model is of strong environmental adaptability and has a good detection effect under the circumstance of various light, different occlusion, different fruits’ distance and number. By comparing the performance of the accuracy with the CenterNet and the SSD models, the results showed that the proposed model was only 1/4 of the size of CenterNet model while has comparable detection accuracy. Compared with the SSD model, the average precision of the proposed model increased by 3.9%, and the model volume decreased by 84.3%. The proposed model runs almost twice as fast using CPU than the CenterNet and SSD models. This study provided a new approach for the research of lightweight model in fruit detection with orchard mobile platform under unstructured environment. Reference | Related Articles | Metrics | Comments（0）

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Design and implementation of intelligent terminal service system for greenhouse vegetables based on cloud service:A case study of Heilongjiang province Zhang Haifeng, Li Yang, Zhang Yu, Song Lijuan, Tang Lixin, Bi Hongwen Smart Agriculture    2019, 1 (3): 87-99.   DOI: 10.12133/j.smartag.2019.1.3.201906-SA002 Abstract （1513）   HTML （1383）    PDF（pc） （5851KB）（1410）       Knowledge map    Save The greenhouse vegetable industry play an important strategic role in the adjustment of agricultural transformation mode and the reform of supply side in Heilongjiang Province. Facility horticulture in Heilongjiang Province develops rapidly in recent years, technical support is in great demand, but the experts' technology support for facility horticulture is far from enough. Experts' on-site guidance costs much time and money in the countryside, while the service efficiency is very low. To solve this urgent problem, the architecture of "greenhouse vegetable intelligent terminal system based on cloud service" and the key technologies of implementation (low-cost IoT, distributed real-time operating architecture, virtual expert service, neural network image recognition and mobile terminal service) were put forward. Based on expert services, supplemented by data mining technology, IoT devices were used as expert's remote perception means, smart phones as user terminals, cloud service for integrating knowledge, resources and Internet of Things data to provide vegetable experts and greenhouse vegetable users with high information acquisition, storage, analysis,decision-making capabilities and effective solutions. Experts could view vegetable production status in greenhouses remotely through the Internet, get image and growth environment data, then provide remote guidance to vegetable farmers through the system, expert knowledge would be stored, mined and reused by the system. The Internet of Things system could automatically send out early warning information by judging the air temperature, humidity, illumination intensity and soil moisture in greenhouse. The application of knowledge map and neural network technology would reduce the workload of experts and increase concurrent processing capability of services at the same time. At present, part of this research has been applied in different user groups such as agricultural research departments, enterprises, vegetable cooperatives and farmers in Heilongjiang Province. The system can provide experts with remote inquiry means of greenhouse vegetable production environment, and has the characteristics of simple deployment and low cost. It is suitable for various greenhouse vegetable scenarios, including fruit and edible fungi. In order to popularize this technology in greenhouse vegetable production in China, and achieve an efficient experts' technical support, this research also proposed technical solutions of a large-scale application scenario through cloud computing in future. Reference | Related Articles | Metrics | Comments（0）

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Study on the Micro-Phenotype of Different Types of Maize Kernels Based on Micro-CT ZHAO Huan, WANG Jinglu, LIAO Shengjin, ZHANG Ying, LU Xianju, GUO Xinyu, ZHAO Chunjiang Smart Agriculture    2021, 3 (1): 16-28.   DOI: 10.12133/j.smartag.2021.3.1.202103-SA004 Abstract （1267）   HTML （126）    PDF（pc） （2085KB）（1405）       Knowledge map    Save Plant micro-phenotype mainly refers to the phenotypic information at the tissue, cell, and subcellular levels, which is an important part of plant phenomics research. In view of the problems of low efficiency, large error, and few traits of traditional methods for detecting kernel microscopic traits, Micro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels. A total of 34 microscopic traits were obtained based on CT sequence images of 7 tissues, including seed, embryo, endosperm, cavity, subcutaneous cavity, endosperm cavity and embryo cavity. Among the 34 microscopic traits, 4 traits, including endosperm cavity surface area, kernel volume, endosperm volume ratio and endosperm cavity specific surface area, were significantly different among maize types (P-value<0.05). The surface area of endosperm cavity and kernel volume of common maize were significantly higher than those of other types of maize. The specific surface area of endosperm cavity of high oil maize was the largest. The endosperm cavity of sweet corn had the smallest specific surface area. The endosperm volume ration of popcorn was the largest. Furthermore, 34 traits were used for One-way ANOVA and cluster analysis, and 11 different maize varieties were divided into four categories, of which the first category was mainly common maize, the second category was mainly popcorn, the third category was sweet corn, and the fourth category was high oil maize. The results indicated that Micro-CT scanning technology could not only achieve precise identification of micro-phenotype of maize kernels, but also provide supports for kernel classification and variety detection, and so on. Reference | Related Articles | Metrics | Comments（0）

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Development and test of a flexible manipulator based on 3D printing Gao Guohua, Dong Zengya, Sun Xiaona, Wang Hao Smart Agriculture    2019, 1 (1): 85-95.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA012 Abstract （1305）   HTML （167）    PDF（pc） （1145KB）（1402）       Knowledge map    Save With the development of computer and automation control technology, robots have gradually entered the field of agricultural production. The application of agricultural robots can improve labor productivity, product quality and working conditions, solve the problem of labor shortage, and promote the intellectualization of agricultural production process. Fruit harvesting is the most time-consuming and laborious part of agricultural production. Since the skin of fruit is relatively fragile, it is easy to cause damage in the process of grasping. Therefore, some flexibility is necessary for the grasping device. As the end of the picking, robot directly acts on the part of the grasping object, the manipulator has attracted more and more attention of scientific researchers because of its light weight, small size, low energy consumption, high flexibility and low cost. Manipulator is the core component of robot, which is installed on the end of picking robot and acting on the object directly. In order to improve universality and flexibility, reduce the damage to the fruits, and shorten the design cycle, the flexible manipulator with simple structure and self-adaptive function was designed to achieve favorable grasp of fruits. The manipulator developed based on 3D printing has the advantages of rapid prototyping, low experimental cost and easy to assemble, etc. Flexible manipulator consists of flexible finger, wrist, base and pneumatic components. Its general action process is opening, grasping, moveing and putting down. However, flexible manipulator combines the two processes of moving and putting down into swallowing, which reduces the execution of the motion and improves the grasping performance and efficiency of the manipulator. Pneumatic components and wrist were printed from flexible materials and the material is thermoplastic urethane and polylactic acid respectively. The wrist is an integral part with flexibility. The use of pneumatic components can achieve the wrist bending, driving flexible fingers self-adaptive deformation to grasp the fruit. The manipulator is placed on the vertical sliding platform of the four-wheel platform, which can move up and down, and the four-wheel platform can move freely in all directions. The single wrist has two rotational degrees of freedom. The kinematics model of single wrist was established by combining constant curvature deformation and D-H coordinate method. On this basis, the functional validation test and safety test of flexible manipulator were carried out. In the safety test, the thin pressure sensor was used as the detection element of the contact force signal between the finger and the grasping object. The experiment results show that the pneumatic components of the flexible manipulator meet the design requirements and the driving wrist is flexible. The manipulator has certain flexibility, and can adapt to the shape of the fruit for self-adaptive grasping. The self-adaptive grasping effect of the manipulator is remarkable, and the fruit skin is intact. Moreover, the flexible manipulator has a favorable self-adaptive function based on the structural design and the complexity of the control system is deduced. In addition, it will provide reference for the design of the flexible grasping mechanism. Reference | Related Articles | Metrics | Comments（0）

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Underwater Fish Species Identification Model and Real-Time Identification System LI Shaobo, YANG Ling, YU Huihui, CHEN Yingyi Smart Agriculture    2022, 4 (1): 130-139.   DOI: 10.12133/j.smartag.SA202202006 Abstract （1307）   HTML （152）    PDF（pc） （1329KB）（1354）       Knowledge map    Save Convolutional neural network models have different advantages and disadvantages, it is becoming more and more difficult to select an appropriate convolutional neural network model in an actual fish identification project. The identification of underwater fish is a challenge task due to varies in illumination, low contrast, high noise, low resolution and sample imbalance between each type of image from the real underwater environment. In addition, deploying models to mobile devices directly will reduce the accuracy of the model sharply. In order to solve the above problems, Fish Recognition Ground-Truth dataset was used to training model in this study, which is provided by Fish4Knowledge project from University of Edinburgh. It contains 27,370 images with 23 fish species, and has been labeled manually by marine biologists. AlexNet, GoogLeNet, ResNet and DenseNet models were selected initially according to the characteristics of real-time underwater fish identification task, then a comparative experiment was designed to explore the best network model. Random image flipping, rotation and color dithering were used to enhance data based on ground-truth fish dataset in response to the limited number of underwater fish images. Considering that there was a serious imbalance in the number of samples in each category, the label smoothing technology was used to alleviate model overfitting. The Ranger optimizer and Cosine learning rate attenuation strategy were used to further improve the training effect of the models. The accuracy and recall rate information of each model were recorded and counted. The results showed that, the accuracy and recall rate of the fish recognition model based on DenseNet reached 99.21% and 96.77% in train set and validation set respectively, its F1 value reached 0.9742, which was the best model obtained in the experiment. Finally, a remote fish identification system was designed based on Python language, in this system the model was deployed to linux server and the Android APP was responsible for uploading fish images via http to request server to identify the fishes and displaying the identification information returned by server, such as fish species, profiles, habits, distribution, etc. A set of recognition tests were performed on real Android phone and the results showed that in the same local area net the APP could show fish information rapidly and exactly within 1 s. Reference | Related Articles | Metrics | Comments（0）

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Time-Varying Heterotypic-Vehicle Cold Chain Logistics Distribution Path Optimization Model LIU Siyuan, CHEN Tian'en, CHEN Dong, ZHANG Chi, WANG Cong Smart Agriculture    2021, 3 (3): 139-151.   DOI: 10.12133/j.smartag.2021.3.3.202108-SA004 Abstract （715）   HTML （51）    PDF（pc） （1122KB）（1346）       Knowledge map    Save In view of the problems of constant speed and single carbon emission calculation method in the distribution model of fresh agricultural products in the transportation link of agricultural supply chain, combined with the time-varying characteristics of road network and the new multi vehicle carbon emission calculation method, this study put forward the distribution route optimization model of fresh agricultural products with four optimization objectives, which were the distribution distance, multi vehicle carbon emission, goods loss and vehicle fixed cost. In this model, the calculation of fuel consumption and carbon emission in the model would be affected by many factors, among which the load is the most important factor: Firstly, the average fuel consumption per 100 km of different trucks was calculated, then the CO2 emission factors of various trucks were calculated according to the carbon balance principle, and finally the average value of the results of each truck was taken as the carbon emission factor of the vehicle. According to those characteristics of the model, an improved double strategies co-evolutionary ant colony system (DC-ACS) was proposed. In this study, the main method was used to transform the problem into a solvable single objective problem. Then, the ant colony algorithm combined the coevolution mechanism, adaptive pheromone update strategy and local search mechanism were used to improve the solution effect of the algorithm. Finally, an appropriate fitness calculation method and stagnation avoidance strategy were designed to enhance the ability of the algorithm to jump out of local optimization. The C105 example of Solomon dataset was solved by using the improved ant colony algorithm. The optimal solutions on the four optimization objectives were 937.94 km, 4961.48 CNY, 4081.78 CNY and 7500.87 CNY respectively, which proved the effectiveness of the model proposed in this study. Based on the effectiveness of the model, the experimental results showed that the total distribution cost of the improved ant colony algorithm reduced by more than 14% on average compared with the basic ant colony algorithm on the four optimization objectives, which proved that the improved ant colony algorithm had more advantages. The improved ant colony algorithm was used to solve large-scale examples with different distributions: centralized, random and mixed. The optimal total costs were 19939.53 CNY, 24095 CNY and 24397.58 CNY, respectively. To sum up, the proposed model and algorithm could provide a good reference for the urban distribution path decision-making of cold chain logistics enterprises, a new idea to improve the distribution path optimization model and optimization method of smart agricultural supply chain, and a reference for enterprises to further expand their scale. Reference | Related Articles | Metrics | Comments（0）

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Identification of Tomato Leaf Diseases Based on Improved Lightweight Convolutional Neural Networks MobileNetV3 ZHOU Qiaoli, MA Li, CAO Liying, YU Helong Smart Agriculture    2022, 4 (1): 47-56.   DOI: 10.12133/j.smartag.SA202202003 Abstract （1844）   HTML （154）    PDF（pc） （1623KB）（1314）       Knowledge map    Save Timely detection and treatment of tomato diseases can effectively improve the quality and yield of tomato. In order to realize the real-time and non-destructive detection of tomato diseases, a tomato leaf disease classification and recognition method based on improved MobileNetV3 was proposed in this study. Firstly, the lightweight convolutional neural network MobileNetV3 was used for transfer learning on the image net data set. The network was initialized according to the weight of the pre training model, so as to realize the transfer and fine adjustment of large-scale shared parameters of the model. The training method of transfer learning could effectively alleviate the problem of model over fitting caused by insufficient data, realized the accurate classification of tomato leaf diseases in a small number of samples, and saved the time cost of network training. Under the same experimental conditions, compared with the three standard deep convolution network models of VGG16, ResNet50 and Inception-V3, the results showed that the overall performance of MobileNetV3 was the best. Next, the impact of the change of loss function and the change of data amplification mode on the identification of tomato leaf diseases were observed by using MobileNetV3 convolution network. For the test of loss value, focal loss and cross entropy function were used for comparison, and for the test of data enhancement, conventional data amplification and mixup hybrid enhancement were used for comparison. After testing, using Mixup enhancement method under focal loss function could improve the recognition accuracy of the model, and the average test recognition accuracy of 10 types of tomato diseases under Mixup hybrid enhancement and focal loss function was 94.68%. On the basis of transfer learning, continue to improve the performance of MobileNetV3 model, the dilated convolution convolution with expansion rate of 2 and 4 was introduced into convolution layer, 1×1 full connection layer after deep convolution of 5×5 was connected to form a perceptron structure in convolution layer, and GLU gating mechanism activation function was used to train the best tomato disease recognition model. The average test recognition accuracy was as high as 98.25%, the data scale of the model was 43.57 MB, and the average detection time of a single tomato disease image was only 0.27s, after ten fold cross validation, the recognition accuracy of the model was 98.25%, and the test results were stable and reliable. The experiment showed that this study could significantly improve the detection efficiency of tomato diseases and reduce the time cost of disease image detection. Reference | Related Articles | Metrics | Comments（0）

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Development and Testing of Intelligent Sensing and Precision Proportioning System of Water and Fertilizer Concentration JIN Zhou​, ZHANG Junqing​, GUO Hongyan, HU Yimin​, CHEN Xiangyu​, HUANG He​, WANG Hongyan​ Smart Agriculture    2020, 2 (2): 82-93.   DOI: 10.12133/j.smartag.2020.2.2.202003-SA012 Abstract （1155）   HTML （1426）    PDF（pc） （2031KB）（1305）       Knowledge map    Save Water and fertilizer integration technology can effectively improve nutrient utilization efficiency. However, the existing water and fertilizer machines have some shortcomings, such as huge cost, single fertilizer injection, need for cleaning water and so on, which hinder the development of water and fertilizer integration technology. Aiming at the problems of precise and low-cost compounding of compound fertilizer at the local farm, the water and fertilizer integrated intelligent irrigation and fertilization system were taken as the research object. In this research, new concept of an intelligent sensing system was proposed， and accurate proportioning system of water and fertilizer concentration was constructed and implemented. Firstly, a fast on-line method of intelligent sensing model of water and fertilizer was established based on a series of concentration gradient compound fertilizer solutions. The conductivity values of these formulated solutions were tested by contactless conductivity detection electrodes. Subsequently, the data analysis algorithms were discussed and compared to fit regression model. Based on the intelligent sensing model of water and fertilizer , the framework structure of in-situ intelligent sensing and accurate proportioning system of water and fertilizer concentration was designed, and the working principle of the system was also explained. The system proposed includs a first-level water and fertilizer concentration intelligent perception model building subsystem and a second-level water and fertilizer accurate proportioning subsystem. The first-level subsystem was designed as a portable device, which mainly included a precise pump for quantitative dosing, a large-range online conductivity sensor, a plastic bucket and supporting control and model building software. The second-level subsystem was designed as a dynamic and precise fertilizer distribution device. The effectiveness of the system was verified by three types of water intelligent fertilizer application so as to guide the in-situ water and fertilizer concentration ratio. The testing results showed that the second-order polynomial fitting curve under regularization conditions was the best model to express the relationship between the conductivity and the concentration of water and fertilizer, and the correlation coefficients R2 was higher than 0.999. Combined with the proportion of each index of compound fertilizer, the concentration of each index of compound fertilizer that the user cares about can be obtained according to this model. The results of three types of water intelligent fertilizer application showed that the conductivity of natural water had an effect on the water and fertilizer system, and the relative deviation was more than 0.1. The online water and fertilizer perception and ratio system proposed in this research realized the elimination of the interference of the local water conductivity on the accuracy of the ratio of water and fertilizer, and the accurate calculation of compound fertilizer was achieved through model calculation. This system has a simple structure and accurate ratio, low cost， and can be easily combined with the existing water and fertilizer integrated machine or artificial fertilizer system. The system could be widely used in facility agriculture, orchard cultivation and field cash crop cultivation, et al. Reference | Related Articles | Metrics | Comments（0）

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Evaluation of fish feeding intensity in aquaculture based on near-infrared machine vision Zhou Chao, Xu Daming, Lin Kai, Chen Lan, Zhang Song, Sun Chuanheng, Yang Xinting Smart Agriculture    2019, 1 (1): 76-84.   DOI: 10.12133/j.smartag.2019.1.1.201812-SA016 Abstract （1988）   HTML （446）    PDF（pc） （1290KB）（1296）       Knowledge map    Save In aquaculture, feeding intensity can directly reflect the appetite of fish, which is of great significance for guiding feeding and productive practice. However, most of the existing fish feeding intensity evaluation methods have problems of low observation efficiency and low objectivity. In this study, a fish feeding intensity evaluation method based on near-infrared machine vision was proposed to achieve an automatic objective evaluation of fish appetite. Firstly, a near-infrared image acquisition system was built by using near-infrared industrial camera. After a series of image processing steps, the gray level co-occurrence matrix was used to extract the texture feature variable information of the image, including contrast, energy, correlation, inverse gap and entropy. Then the data set were constructed by using these five feature variables as input vectors, and the support vector machine classifier was trained. Among them, the optimal penalty coefficient c and kernel function parameter g were selected by grid search. Finally, the trained images were used to classify the feeding images of fish. And ultimately, the evaluation of fish feeding intensity was realized. The results show that the accuracy of the evaluation could reach 87.78%. In addition, this method does not need to consider the impact of reflections, sprays and other factors on image processing results, so it has strong adaptability and can be used for automatic and objective evaluation of fish appetite, thus provide theoretical basis and methodological support for subsequent feeding decisions. Reference | Related Articles | Metrics | Comments（0）

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From Stand to Organ Level—A Trial of Connecting DSSAT and GreenLab Crop Model through Data WANG Xiujuan, KANG Mengzhen, HUA Jing, DE REFFYE Philippe Smart Agriculture    2021, 3 (2): 77-87.   DOI: 10.12133/j.smartag.2021.3.2.202103-SA006 Abstract （780）   HTML （34）    PDF（pc） （2152KB）（1286）       Knowledge map    Save Crop models involve complex plant processes, which can be built in different scales of space and time, from molecule, cell, organ, tissue, individual to stand in space and from second to year in time. Based on different research requirements, switching the model scales can make the applicability of the model more extensive and flexible. How to switch the crop model from stand level to organ level is the content of this research. The DSSAT software (stand level) and functional-structural plant model 'GreenLab' (organ level) were chosen to explore the possibility to switch the crop model from stand to organ level. The DSSAT can simulate the growth and development processes of crops in detail according to the growth period by taking the data of weather, soil, crop management, and observational data as input. The GreenLab can simulate the growth and development and their interaction of crops by considering plant structure, and the model parameters can be estimated according to the measurements. In this study, the experimental data contains two parts: the measurements of four maize cultivars with two treatments (irrigated and rainfed) in DSSAT, and the simulations including the weights of leaves, internodes and fruits per day using DSSAT based on the measurements. The simulation results of DSSAT were used to calibrate the parameters of the environmental (E), sink strength (Po), and remobilization (kb and ki) in GreenLab, and to compute the weights of leaves, internodes and fruits for each phytomer. The simulation results of the GreenLab model were compared and analyzed with the experimental data and the simulations of DSSAT. The consistency of calculation results could be an indicator to explore the method of building an interface between different-scale crop models, and to compare the characteristics of different models. The results showed that the GreenLab model could reproduce the simulation data of the DSSAT and the measurement data, including the leaf area index (LAI) and the total weight of the plants, and further could compute the biomass for each organ (leaf, internode and fruit), and the biomass distribution among organs, the biomass production (Q), the demand (D) and the ratio between Q and D during the growth. Therefore, the detailed information of organ growth and development could be reproduced and the 3D structures of plant could be given. Finally, the advantages and application fields of different-scale model integration were discussed. Reference | Related Articles | Metrics | Comments（0）

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State-of-the-Art and Prospect of Automatic Navigation and Measurement Techniques Application in Conservation Tillage WANG Chunlei, LI Hongwen, HE Jin, WANG Qingjie, LU Caiyun, CHEN Liping Smart Agriculture    2020, 2 (4): 41-55.   DOI: 10.12133/j.smartag.2020.2.4.202002-SA002 Abstract （1426）   HTML （1715）    PDF（pc） （2504KB）（1256）       Knowledge map    Save Intelligent technology is one of the important approaches to improve working quality and efficiency of conservation tillage machine. Automatic navigation and measurement & control technology, which are the key components of intelligent technology, have been rapidly developed and applied in conservation tillage. In this paper, the application progress of automatic navigation and measurement & control technology in conservation tillage, including automatic guidance technology, operation monitoring technology for operating parameters and operation controlling technology of conservation tillage machine were reviewed. Firstly, wheat-maize planting mode was taken as an example to expound the automatic guidance technology for conservation tillage machine due to many types of crop planting modes under conservation tillage. According to the principle of navigation, it could be divided into automatic guidance technology of touch type, automatic guidance technology of machine vision type and automatic guidance technology of GNSS type. From these different automatic guidance technologies for no/minimum tillage seeding in maize stubble field, the application progress of automatic navigation technology in conservation tillage machine was introduced in detail. Secondly, the development of the operation monitoring technology for operating parameters of conservation tillage machine was systematically presented as follows: 1) The rapid detection technology for surface straw coverage, including surface straw coverage before and after operation, which was of great significance for the determination of conservation tillage technology and the evaluation of the performance of the conservation tillage machine; 2) The monitoring technology for seeding parameters of no/minimum tillage planter, mainly contained seeding quantity, missed seeding and multiples seeding, which were the key indicators for seeding quality; 3) The monitoring technology for operating area of conservation tillage machine, which was mainly calculated based on the forward speed of the testing machine. Thirdly, the development status of operation controlling technology for conservation tillage machine was reviewed, mainly focusing on the compensation and controlling technology for missed seeding and operation depth controlling technology. The operation controlling technology for conservation tillage machine, which was capable of realizing certain active control of the machine key components under the condition of accurate and real-time monitoring of the current operation status of conservation tillage machine, was important for working quality. To be specific, the operation depth controlling technology was composed of seeding depth, subsoiling depth and topsoil tillage depth. In the end, on the basis of summarizing the current application of automatic navigation and measurement technology in conservation tillage, the future research directions of automatic guidance technology, operation monitoring technology for operating parameters, and operation controlling technology in conservation tillage machine were prospected. Reference | Related Articles | Metrics | Comments（0）

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Design and Test of Disinfection Robot for Livestock and Poultry House FENG Qingchun, WANG Xiu, QIU Quan, ZHANG Chunfeng, LI Bin, XU Ruifeng, CHEN Liping Smart Agriculture    2020, 2 (4): 79-88.   DOI: 10.12133/j.smartag.2020.2.4.202010-SA005 Abstract （1480）   HTML （628）    PDF（pc） （2302KB）（1246）       Knowledge map    Save In order to improve the efficiency and safety of epidemic prevention and disinfection operations for livestock and poultry breeding, the disinfection robot system and the automatic disinfecting mode were researched in this study. The robot system is composed of four components, namely the automatic bearing vehicle, the disinfection spraying unit, the environmental monitoring sensors, and the controller. The robot supports two working modes: fully automatic mode and remote control mode. Aiming at the low-light and low-stress condition in the livestock and poultry houses, the method for detecting navigation path based on "Magnet-RFID" marks in the ground was proposed to realize the robot's automatic moving between the cages. In view of the large-flow and long-range requirements of the disinfectant's spraying, the air-assisted nozzle was designed, which could atomize and disperse the liquid independently. Based on the CFD simulation of airflow in the nozzle, the nozzle's parts structural parameters were optimized, as the angle of the cone-shaped guide pad and the inclination angle of the grid respectively determined as 75°and 90°. Finally, the robot's performance was tested in a poultry house in Beijing. The results showed that, the robot's mobile platform could automatically navigate at the speed of 0.1－0.5 m/s, and the maximal deviation distance between the actual trajectory and the expected path was 50.8 mm. The air-assisted nozzle could realize the atomization and diffusion of the liquid medicine at the same time, and was suitable for spraying the liquid medicine with a flow rate of 200－400 mL/min. The diameter (DV.9) of the liquid droplets formed was 51.82－137.23 μL, and became larger as the flow rate of the liquid medicine increased. The deposition density of spray droplets formed by the nozzle was 116－149/cm2, and decreased as the spray distance increased. The size and density of the liquid droplets of the spray nozzle in different areas of the cage all met the index requirements for effectively killing adherent pathogenic microorganisms. The robot could be applied as an automatic sprayer for disinfectant and immune reagent in the livestock and poultry house. Reference | Related Articles | Metrics | Comments（0）

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A fast extraction method of broccoli phenotype based on machine vision and deep learning Zhou Chengquan, Ye Hongbao, Yu Guohong, Hu Jun, Xu Zhifu Smart Agriculture    2020, 2 (1): 121-132.   DOI: 10.12133/j.smartag.2020.2.1.201912-SA003 Abstract （1420）   HTML （927）    PDF（pc） （1813KB）（1235）       Knowledge map    Save How to accurately obtain the area and freshness of broccoli head in the field condition is the key step to determine broccoli growth. However, the rapid segmentation and grading of broccoli ball remains difficult due to the low equipment development level. In this research, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli ball. By constructing a private image dataset with 442 of broccoli-ball images (acquired using a self-developed imaging system) under controlled conditions, a deep convolutional neural network named “Improved ResNet” was trained to extract the broccoli pixels from the background. The technical process of our method includes: (1) take the orthophoto images of broccoli head based on a near-ground image acquisition platform and establish the original data set; (2) preprocess the training images and input the model for segmentation; (3) use the PSOA and Otsu algorithm for fine segment based on color characteristics to obtain the freshness information. The experimental results demonstrated that the precision of the segmentation model is about 0.9 which is robust to the interference of soil reflectance fluctuation, canopy shadow, leaf occlusion and so on. Our experiments showed that a combination of improved ResNet and PSOA method got higher broccoli balls segmenting and grading precision. One major advantage of this approach is that dealing with only a few images, reducing the data volume and memory requirements for the image processing. All of the methods were evaluated using ground-truth data from three different varieties, which we also make available to the research community for subsequent algorithm development and result comparison. Compared with other 4 approaches, the evaluation results shows better performance regarding the segmentation and grading accuracy. The results of SSIM, precision, recall and F-measure by using Improved ResNet were about 0.911, 0.897, 0.908 and 0.907 respectively, which were 10%~15% higher than the traditional approaches. In addition, on the basis of the segmentation results, PSO-Otsu method was proved that it can be used to achieve a quickly analysis to the freshness of the ball, with the mean accuracy of 0.82. Overall, the proposed method is a high-throughput method to acquire multi-phenotype parameters of broccoli in field condition, which can support the research of broccoli field monitoring and traits tracking. Reference | Related Articles | Metrics | Comments（0）

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A Fluorescence Based Dissolved Oxygen Sensor GU Hao​, WANG Zhiqiang​, WU Hao​, JIANG Yongnian​, GUO Ya Smart Agriculture    2020, 2 (2): 48-58.   DOI: 10.12133/j.smartag.2020.2.2.202005-SA004 Abstract （1298）   HTML （300）    PDF（pc） （1763KB）（1234）       Knowledge map    Save The measurement of dissolved oxygen content in water is of great significance to aquaculture. However, the dissolved oxygen sensors on the market in China are expensive, and are difficult to maintain continuous online measurement and update parts, so they cannot be widely applied in real production and play expected role in the aquaculture Internet of things(IoT). Based on the principle of fluorescence quenching, a low cost and easy maintenance of dissolved oxygen sensor was developed in this work based on the relationship between the concentration of dissolved oxygen in water and the phase difference of fluorescence signal. The self-made oxygen-sensitive membrane was used to generate red fluorescence which being excited by blue light, and the fluorescence life was regulated by the concentration of dissolved oxygen. Photoelectric conversion circuit with optical signal sensing device was designed to sense optical signal. The STM32F103 microprocessor was used as the main control chip, and the lower computer program was programmed to generate the excitation light pulse. The phase-sensitive detection principle and fast Fourier transform (FFT) were used to calculate the phase difference between the excitation light and the reference light, which was converted into the concentration of dissolved oxygen and realized the measurement of dissolved oxygen. The fluorescence detection part and the main control part of the system were designed as detachable independent modules, and shield lines were used to plug and pull directly, so as to facilitate replacement and maintenance and realize online remote measurement. The testing results showed that, the measurement range of the sensor was 0-20 mg/L, system time delay was less than 2 s, and the life time of the oxygen sensitive membrane would be about 1 year. The dissolved oxygen sensor has the characteristics of convenient measurement, stable result output, low cost and small volume, which will lay a good foundation for the development and marketization of low-cost dissolved oxygen sensors in aquaculture industry of China. Reference | Related Articles | Metrics | Comments（0）

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Distilled-MobileNet Model of Convolutional Neural Network Simplified Structure for Plant Disease Recognition QIU Wenjie, YE Jin, HU Liangqing, YANG Juan, LI Qili, MO Jianyou, YI Wanmao Smart Agriculture    2021, 3 (1): 109-117.   DOI: 10.12133/j.smartag.2021.3.1.202009-SA004 Abstract （1429）   HTML （106）    PDF（pc） （1643KB）（1232）       Knowledge map    Save The development of convolutional neural networks(CNN) has brought a large number of network parameters and huge model volumes, which greatly limites the application on devices with small computing resources, such as single-chip microcomputers and mobile devices. In order to solve the problem, a structured model compression method was studied in this research. Its core idea was using knowledge distillation to transfer the knowledge from the complex integrated model to a lightweight small-scale neural network. Firstly, VGG16 was used to train a teacher model with a higher recognition rate, whose volume was much larger than the student model. Then the knowledge in the model was transfered to MobileNet by using distillation. The parameters number of the VGG16 model was greatly reduced. The knowledge-distilled model was named Distilled-MobileNet, and was applied to the classification task of 38 common diseases (powdery mildew, Huanglong disease, etc.) of 14 crops (soybean, cucumber, tomato, etc.). The performance test of knowledge distillation on four different network structures of VGG16, AlexNet, GoogleNet, and ResNet showed that when VGG16 was used as a teacher model, the accuracy of the model was improved to 97.54%. Using single disease recognition rate, average accuracy rate, model memory and average recognition time as 4 indicators to evaluate the accuracy of the trained Distilled-MobileNet model in a real environment, the results showed that, the average accuracy of the model reached 97.62%, and the average recognition time was shortened to 0.218 s, only accounts for 13.20% of the VGG16 model, and the model size was reduced to only 19.83 MB, which was 93.60% smaller than VGG16. Compared with traditional neural networks, distilled-mobile model has a significant improvement in reducing size and shorting recognition time, and can provide a new idea for disease recognition on devices with limited memory and computing resources. Reference | Related Articles | Metrics | Comments（0）

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Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor Wan Liang, Cen Haiyan, Zhu Jiangpeng, Zhang Jiafei, Du Xiaoyue, He Yong Smart Agriculture    2020, 2 (1): 58-67.   DOI: 10.12133/j.smartag.2020.2.1.201911-SA002 Abstract （1553）   HTML （2054）    PDF（pc） （1511KB）（1228）       Knowledge map    Save Water concentration is a key parameter to characterize crop physiological and healthy status. It is of great significance of employing unmanned aerial vehicle (UAV) low-altitude remote sensing technology to predict crop water concentration for crop breeding and precision agriculture management. UAV remote sensing has been widely used for monitoring crop growth status, mainly focusing on using vegetation indices to estimate crop growth parameters at single or several growth stages. Few studies have been performed on evaluating crop water concentration. Consequently, this study mainly used vegetation indices and texture features extracted from UAV-based RGB and multispectral images to monitor water concentration of rice crop during the whole growth period. Firstly, a multi-rotor UAV equipped with high-resolution RGB and multispectral cameras to collect canopy images of rice crop, and water concentration was also measured by ground sampling. Then, vegetation indices and texture features calculated from RGB and multispectral images were used to analyze the growth changes of rice. Finally, random forest regression method was used to establish a prediction model of water concentration based on different image features. The results show that: (1) vegetation index, texture features and ground-measured water concentration could be used to dynamically monitor rice growth, and there existed correlations among these parameters; (2) image features extracted from multispectral images possessed more potential than those from RGB images to evaluate water concentration of rice crop, and normalized difference spectral index NDSI771, 611 achieved the best prediction accuracy (R2 = 0.68, RMSEP = 0.039, rRMSE = 5.24%); (3) fusing vegetation indices and texture features could further improve the prediction of water concentration (R2 = 0.86, RMSEP = 0.026, rRMSE = 3.21%), and the prediction error of RMSEP was reduced by 16.13% and 18.75%, respectively. These results demonstrats that it is feasible to apply UAV-based remote sensing to monitor water concentration of rice crop, which provides a new insight for precision irrigation and decision making of field management. Reference | Related Articles | Metrics | Comments（0）

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Short-Term Price Forecast of Vegetables Based on Combination Model of Lasso Regression Method and BP Neural Network YU Weige, WU Huarui, PENG Cheng Smart Agriculture    2020, 2 (3): 108-117.   DOI: 10.12133/j.smartag.2020.2.3.202008-SA003 Abstract （1249）   HTML （447）    PDF（pc） （1295KB）（1213）       Knowledge map    Save Vegetables are an important part of residents' diet. The abnormal fluctuation of vegetable prices has caused losses to the economic interests of vegetable farmers and also affected the daily diet and quality of life of residents. However, there are some difficulties in vegetable price prediction, such as large price fluctuation and complicated influencing factors. Cucumber is the main category of vegetables and a common food on the daily table of residents and its recent price fluctuations have aroused widespread concern. In this research, taking cucumber as the research object, a combination model (L-BPNN) combining Lasso regression method and BP neural network was constructed to forecast the short-term price of cucumber. Firstly, the factors affecting the price of cucumber, such as supply, demand and circulation were analyzed. Then the price fluctuation characteristics of cucumber in China from 2010 to 2018 were analyzed and 24 factors were selected as the influencing factors of cucumber price. In the case of complex factors, Lasso regression was used to compress the 24 input influencing factors and the 12 remaining influencing factors with large correlation degree after compression were used as the input influencing factors of BP neural network. Among the 12 related factors , the positive effects included: land cost, per capita disposable income of urban residents, urban vegetable consumption price index, fuel surcharge, booth fee, packaging and processing fee, inflation rate, affected area and temperature deviation from normal value; negative effects included sown area, industrial support amount and average temperature. On this basis, a combination model combining Lasso regression method with BP neural network (L-BPNN) was constructed to forecast the short-term price of cucumber. The neural network was used to train and adjust the model between the input influencing factors and the output price. Compared with the regression analysis and intelligent analysis methods, the results show that the average relative error of L-BPNN combination model was the smallest, only 0.66%, which was 64.52%, 82.11% and 86.2% lower than Lasso regression model, BP neural network model and RBF neural network model respectively, and had higher prediction accuracy. The results of this study realizes the short-term price forecast of cucumber, and can also be extended to other vegetable varieties, which is of great significance for guaranteeing the income of vegetable farmers and stabilizing the market price of vegetables. Reference | Related Articles | Metrics | Comments（0）

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Research Progress of Key Technologies and Verification Methods of Numerical Modeling for Plant Protection Unmanned Aerial Vehicle Application TANG Qing, ZHANG Ruirui, CHEN Liping, LI Longlong, XU Gang Smart Agriculture    2021, 3 (3): 1-21.   DOI: 10.12133/j.smartag.2021.3.3.202107-SA004 Abstract （818）   HTML （92）    PDF（pc） （2594KB）（1207）       Knowledge map    Save With the increasing application of plant protection unmanned aerial vehicle (UAV) in precision agriculture, the numerical simulation methods for the development of the downwash flow field of the plant protection UAV and the deposition and drift process of droplets affected by the downwash flow field have achieved rapid and diversified development, but the advantages, disadvantages, scope of application, and verification of each method still lack a systematic review. This article discusses the inviscid model, computational fluid dynamics model and lattice Boltzmann model (LBM) respectively. The advantage of the inviscid wake vortex model based on the vortex element method is that the calculation process is simple. Moreover, integrated with the most widely used aerial spray drift prediction software AGricultural DISPersal (AGDISP), it can be a promising way to do real-time UAV spray drift prediction. But due to lack of viscosity and turbulence models, the droplet deposition and drift simulation accuracy of inviscid model is relatively lower than other models. The computational fluid dynamics (CFD) model includes the finite volume method (FVM) and the finite difference method (FDM). The FVM in the computational fluid dynamics model has high robustness and can be applied to the simulation of various complex environments. Many commercial CFD software are based on FVM and achieved a fast development in aerial spray modeling recently. However, the FVM is greatly affected by the quality of the mesh, and its commonly used upwind style has limited accuracy (second-order accuracy). Under the same mesh density, it is easier to generate artificial dissipation when simulating the rotor tip vortex than the finite difference method. As a result, the simulated rotor tip vortex dissipation speed is much faster than the actual situation. Compared with the FVM, the structured grid used in the FDM is easier to construct a high-order precision numerical format. Which can reach 4-5 orders of accuracy, and with adaptive grid technology, FDM can simulate the evolution of rotor tip vortex with high temporal and spatial accuracy, and can reproduce the typical flow structure development process of the real rotor downwash flow field. However, it also has problems such as high grid structure requirements and excessive computing power requirements. LBM has advantages in computing three-dimensional flow field problems with complex boundary conditions and non-stationary moving objects. However, there are still shortcomings in its functional diversity and completeness. The accuracy of the numerical models mentioned above still needs field test and indoor experiment such as high-speed Particle Image Velocimetry (PIV)/ Phase Doppler Interferometry (PDI) method to verify and optimize. The authors finally pointed out the future direction of plant protection UAV application simulation and verification. Reference | Related Articles | Metrics | Comments（0）

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Effects of technical operation parameters on spray characteristics of rotor plant protection UAV Zhu Hang, Li Hongze, Huang Yu, Yu Haitao, Dong Yunzhe, Li Junxing Smart Agriculture    2019, 1 (3): 113-122.   DOI: 10.12133/j.smartag.2019.1.3.201906-SA001 Abstract （1148）   HTML （328）    PDF（pc） （3184KB）（1204）       Knowledge map    Save High-quality operation of plant protection UAV is the premise of precision operation in agricultural aviation, so it is particularly important to study the characteristics of spray system. In order to explore the factors that affect the spray quality, the comprehensive experimental platform of spray performance (developed by Jilin Agricultural Machinery Research Institute) was used to test the droplet deposition distribution and droplet diameter under different UAV rotor speed, spray height and centrifugal nozzle speed in this research, and regression analysis on the deposition characteristics and particle diameter data of 12 groups of tests was conducted. The results showed that the three repeated tests of the same set of parameters had good consistency. Droplets had obvious drift and the maximum effective deposition rate was 46.31% and minimum 31.74%, which shows that the effective deposition rate of droplets was lower than 50%. Compared with the regression analysis results of droplet diameters DV10, DV50 and DV90, the spray height P value is greater than 0.5, and the nozzle speed and rotor speed P value are less than 0.5. So it can be inferred that spray height had a very significant effect on deposition, no significant effect on droplet size. The nozzle speed and rotor speed had very significant effect on droplet size, no significant effect on deposition. The test results of this research can provide theoretical basis and data support for improving the operation quality and spraying efficiency of UAVs. 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）（1198）       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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Multi-blockchain application technology for agricultural products transaction Liang Hao, Liu Sichen, Zhang Yinuo, Lv Ke Smart Agriculture    2019, 1 (4): 72-82.   DOI: 10.12133/j.smartag.2019.1.4.201907-SA001 Abstract （1320）   HTML （895）    PDF（pc） （1340KB）（1197）       Knowledge map    Save Agriculture of China is a typical agricultural system of small producer and large market, producers are too scattered. Agricultural foundation remains weak in the vast rural areas, especially poverty-stricken areas. Blockchain technology has good complementarity and applicability with China's agricultural products trading system, because of its distributed storage, transaction information transparency and product information traceability. However, the agricultural product trading system has characteristics of product diversity, commercial process complexity, user group widespread, decentralized, privacy protection and so on. It is difficult to apply the traditional blockchain technology directly to China's agricultural products trading information network. In view of the above problems, the design idea of alliance chain was adopted, and the technology of multi-chain agricultural product transaction information, which includes transaction information blockchain, user information blockchain and agricultural products information blockchain was put forward. The product information blockchain provided the detailed information of agricultural products and guarante that the traceability and non-tamperability of the information. The blockchain node access mechanism was introduced in the user information chain to provide real-name voucher registration and management functions for the agricultural product trading platform. The transaction information blockchain recorded the results of all transaction smart contracts, and through the addition of channel technology, different transaction information could be isolated from each other, which could meet the privacy protection of transaction information and user data and the rapid processing of transaction data. The profit of the transaction was automatically divided by the smart contract, which improved the efficiency of execution and reduces the transaction cost. Finally, a transparent, efficient and applicable blockchain framework for agricultural product transactions was established. 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 （1095）   HTML （205）    PDF（pc） （1097KB）（1181）       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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Effects on Control Efficacy of Pesticide-Adjuvants Mixture against Rice Chilo Suppressalis(walker) Based on Plant Protection Unmanned Aerial Vehicle ZI Le, ZANG Yu, HUANG Junhao, BAO Ruifeng, ZHOU Zhiyan, XIAO Hanxiang Smart Agriculture    2021, 3 (3): 52-59.   DOI: 10.12133/j.smartag.2021.3.3.202105-SA007 Abstract （589）   HTML （29）    PDF（pc） （877KB）（1180）       Knowledge map    Save To explore the effect of pesticide-adjuvants mixture on the control efficacy against Rice Chilo Suppressalis(walker). This study designed a three-factor, three-level orthogonal experiments with pesticides (10% emamectin benzoate·indoxacard SC, 5% chlorantraniliprole SC and 0.8% rotenone SC), adjuvants(organosilicon, lecithin and mineral Oil) , spray volume (21,24 and 27 L/hm2), referred to the three-factor, three-level orthogonal experimental scheme. And made the blank factor the deviation to analyze its rationality. Analysis of variance (ANOVA) statistical method was used to analyze the significance level of each factor. Duncan's new multiple range test (DMRT) method was used to analyze the order of the influence of different levels of each factor on the control efficacy against Rice Chilo Suppressalis(walker). The results showed that, under the experiment conditions of this research, the mean square value of the deviation factor was smaller than the mean square value of the pesticides, the adjuvants and the spray volume, and the deviation of the orthogonal experiment was within a reasonable range. The main order of the effect of the three factors on the control efficacy of Rice Chilo Suppressalis(walker) was: adjuvants > pesticides > spray volumn. On the 14th day after spraying, pesticides showed a significant effect on the control efficacy (P<0.05) and adjuvants showed a highly significant effect on the control efficacy (P<0.01), and spray volume showed no significant effect on the control efficacy. On the 14th day after spraying, the level 3 of the factor "pesticides" was more effective, in the order of Rotenone > Chlorantraniliprole > Emamectin Benzoate·Indoxacard. The level 1 of the factor "adjuvants" was more effective, in the order of Organosilicon > Lecithin > Mineral Oil. The level 3 of the factor "spray volume" was more effective, in the order of 27 L/hm2 > 24 L/hm2 > 21 L/hm2. Therefore, a preferred pesticide-adjuvants mixture method was 0.8% rotenone SC, organosilicon adjuvants and 27 L/hm2 of spray volume, which had a rapid and long-lasting control efficacy, and its control efficacy in the field reached 81.45% on the 14th day after spraying. Additionally, there was also a satisfactory pesticide-adjuvants mixture method that was 5% Chlorantraniliprole, organosilicon adjuvants and 24 L/hm2 of spray volume. This mixture method also performed well, achieving 79.3% control efficacy in the field on the 14th day after spraying. This study could provide a reference for the optimization of the mixture methods of solutions (pesticides, adjuvants and spray volume) for controlling Rice Chilo Suppressalis(walker). Reference | Related Articles | Metrics | Comments（0）

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Application Analysis and Prospect of Nanosensor in the Quality and Safety of Agricultural Products WANG Peilong , TANG Zhiyong Smart Agriculture    2020, 2 (2): 1-10.   DOI: 10.12133/j.smartag.2020.2.2.202003-SA003 Abstract （1409）   HTML （1430）    PDF（pc） （1634KB）（1173）       Knowledge map    Save Nano materials with special size effect and excellent photoelectric properties have been highly valued and widely used in sensing analysis for greatly improving the performance of sensor analysis technology. In recent years, with the rapid development of smart agriculture, the quality and safety of agricultural products as an important part of agricultural production have attracted more and more attentions. There are many harmful ingredients, including pesticides, veterinary drugs, mycotoxins, and environmental contaminants etc, can potentially affected the quality and safety of agricultural products. Therefore, high performance analytical methods and sensing technologies are essential. Thanks to the emerging of nano materials, they provide a novel approach to improve the analytical performances of the sensing technologies. Furthermore, the sensors based on nano materials have also been utilized into monitoring the harmful substances in agricultural products. This review briefly described the properties and characteristics of several commonly used nano materials, including carbon nano materials, noble metal based nano materials and metal-organic framework materials, follow discussed on the common sensing and analysis technologies and devices based on nano materials, such as chemical sensor, biosensor, electrochemical sensor and spectral sensor, as well as the application of nano sensing technology in the quality and safety monitoring of agricultural products. Especially, the function of nano materials in sensors and analytical performances of the developed sensors had been discussed in detailed. Chemical sensor devices had the characteristics of fast response speed and high sensitivity. They were widely used in environmental monitoring, food safety and medical diagnosis, such as monitoring hazardous substances, clenbuterol and melamine, metronidazole, dioxins, etc. Biosensors were widely used to monitor prohibited additives, mycotoxins, and so on. Electrochemical sensors were typically equipped with miniaturized analysis equipment, which detected trace targets, including small organic molecules, metal ions and biomolecules, by measuring changed in current and other electrochemical signals. This article introduced surface-enhanced Raman spectroscopy (SERS) , which was one of spectral sensor, and its applications. SERS technology had the advantages of good sensitivity, single molecule detection capability and rich spectral information. It had become a promising spectral technology in the rapid sensing analysis of target objects, and is developing rapidly in the fields of food safety, environmental monitoring and health. Finally, the existing problems of nano sensing and analysis technology, such as achievement of high-performance nano materials, fabrication of sensing devices and construction of high flux sensing arrays were summarized. The development trend and prospect of nanosensor were also discussed. It is believed that the review could provide a lot of useful information for the readers to understand the development of sensing technology for the quality and safety of agricultural products. Reference | Related Articles | Metrics | Comments（0）

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Regionalization research of summer corn planting in North China Plain based on multi-source data Diao Xingliang, Yang Zaijie, Li Qifeng, Yu Jingxin, Zheng Wengang, Shi Leigang Smart Agriculture    2019, 1 (2): 73-84.   DOI: 10.12133/j.smartag.2019.1.2.201901-SA002 Abstract （1212）   HTML （439）    PDF（pc） （1101KB）（1170）       Knowledge map    Save Accurate identification of agricultural production environment information and agricultural production characteristics, comprehensive classification of meteorological, soil and crop multi-source data, are the bases for improving the efficiency of agricultural resource utilization and optimizing the structure of agricultural cultivation. Based on the meteorological data of nearly 20 years and the statistics of com yield, this study first constructed a database of spatial and temporal distribution characteristics of climate resources and com production in North China Plain, and there were significant spatiotemporal changes in rainfall, activity accumulated temperature, sunshine hours, solar radiation and corn yield. By using the method of fine crop planting regionalization, the summer com planting areas in the North China plain were divided into 5 categories: the extremely unsuitable area, the unsuitable area, the less suitable area, the suitable area, and the most suitable area, the proportions of each type of area in the total area is about 10%, 11%, 25%, 30%, 24%, respectively, further through using the Environmental Category attribution analysis method, each large class was divided into 5 subcategories, the probability was greater than 75% the relatively stable region accounts for about 63% of the total area, the fluctuation area of less than 75% is about the stable spatial and temporal distribution of 37%; the extremely unsuitable area, the unsuitable area and the less suitable area, these three kinds of spatial and temporal distributions were relatively stable, the belonging degree was 100%, accounting for 87.67%, 70.41% and 84.28%, respectively, the fluctuation zone mainly occurs between the extremely suitable zone and the suitable zone, and between the suitable zone and the relatively suitable zone. The fine zoning of summer com in North China Plain has important guiding significance for improving the utilization efficiency of local resources and optimizing the layout of com industry. Reference | Related Articles | Metrics | Comments（0）

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Recognition and localization method of occluded apples based on K-means clustering segmentation algorithm and convex hull theory Jiang Mei, Sun Sashuang, He Dongjian, Song Huaibo Smart Agriculture    2019, 1 (2): 45-54.   DOI: 10.12133/j.smartag.2019.1.2.201903-SA003 Abstract （1192）   HTML （445）    PDF（pc） （1240KB）（1151）       Knowledge map    Save Accurate segmentation and localization of apple objects in natural scenes is an important part of wisdom agriculture research for information perception and acquisition. In order to solve the problem that apples recognition and positioning are susceptible to occlusion of leaves in natural scenes, based on the K-means clustering segmentation algorithm, the object recognition algorithm based on convex hull theory was proposed. And the algorithm was compared with the object recognition algorithm based on removing false contours and the full-contour points fitting object recognition algorithm. The object recognition algorithm based on convex hull theory utilized that apples were like circle, combining K-means algorithm with Otsu algorithm to separate fruit from background. The convex polygon was obtained by convex hull theory and fit it circle to determine the position of the fruit. To verify the effectiveness of the algorithm, 157 apple images in natural scenes were tested. The average overlap rates of the object recognition algorithm based on convex hull theory, the object recognition algorithm based on removing false contour points and the full-contour points fitting object recognition algorithm were 83.7%, 79.5% and 70.3% respectively, the average false positive rates were 2.9%, 1.7% and 1.2% respectively, and the average false negative rates were 16.3%, 20.5% and 29.7% respectively. The experimental results showed that the object recognition algorithm based on convex hull theory had better localization performance and environmental adaptability compared to the other two algorithms and had no recognition error, which can provide reference for occluded fruits segmentation and localization in the natural scenes. 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）（1145）       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 （873）   HTML （280）    PDF（pc） （3579KB）（1145）       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 （837）   HTML （117）    PDF（pc） （863KB）（1134）       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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An improved method for estimating dissolved oxygen in crab ponds based on Long Short-Term Memory Zhu Nanyang, Wu Hao, Yin Daheng, Wang Zhiqiang, Jiang Yongnian, Guo Ya Smart Agriculture    2019, 1 (3): 67-76.   DOI: 10.12133/j.smartag.2019.1.3.201905-SA004 Abstract （1242）   HTML （141）    PDF（pc） （2818KB）（1128）       Knowledge map    Save Dissolved oxygen (DO) is vital to aquaculture industry and affects the yield of aquaculture. Low DO in water can lead to death of crabs, therefore, it is important to measure DO accurately. However, the DO sensors are usually expensive and often lost function due to corrosion in water environmental and adsorption of different materials on their surface, which result in the inaccuracy in measured DO values. It is thus important to develop effective methods to estimate DO concentrations by using other environmental variables, which may reduce farmers' cost because DO sensors are not used. In this research, the collected environmental data, including temperature, pH, ammonia nitrogen, turbidity, were used to estimate DO concentrations in crab ponds. The data were preprocessed to eliminate missing values and outlier. Correlation analysis was applied to determine the relationship between environmental variables (temperature, pH, ammonia nitrogen, turbidity) and DO to show the rationale of using these four variables to forecast DO concentration. Principal component analysis was used to reduce the dimension of environmental data to reduce computation cost. For DO concentration estimation, it is more important to make the estimation of DO concentration at low values more accurate because DO concentration at low values is dangerous to crabs. This implies that estimation of DO concentrations at low or high values should be treated differently and applied different rates. Based on the Long Short-Term Memory (LSTM), a low DO concentration estimation model of Low Dissolved Oxygen Long Short-Term Memory(LDO-LSTM), which can improve the estimation accuracy of low DO values was proposed by optimizing the loss function of LSTM back propagation. The loss function of LDO-LSTM was based on the Mean Absolute Percentage Error (MAPE). According to the trend of DO, the true DO and the estimated DO values were applied weight functions. The Root Mean Square Error (RMSE) and the MAPE were used to evaluate the performance of LDO-LSTM and LSTM in DO estimation. Experimental results show that the value of RMSE and MAPE were stable at about 0.1 for LSTM and LDO-LSTM in forecasting DO when dissolved oxygen was higher than 6mg/L and the value of RMSE and MAPE of LDO-LSTM were lower than LSTM by 0.25 and 0.139. The results prove that the proposed method can not only provide desirable estimation accuracy for DO concentrations at high values but also make the estimated DO concentrations at low values more accurate. This research is expected very useful in reducing aquaculture costs and improving accuracy in forecasting DO especially at low values. 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）（1122）       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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Method of tomato leaf diseases recognition method based on deep residual network Wu Huarui Smart Agriculture    2019, 1 (4): 42-49.   DOI: 10.12133/j.smartag.2019.1.4.201908-SA002 Abstract （1729）   HTML （919）    PDF（pc） （1077KB）（1114）       Knowledge map    Save Intelligent recognition of greenhouse vegetable diseases plays an important role in the efficient production and management. The color, texture and shape of some diseases in greenhouse vegetables are often very similar, it is necessary to construct a deep neural network to judge vegetable diseases. Based on the massive image data of greenhouse vegetable diseases, the depth learning model can automatically extract image details, which has better disease recognition effect than the artificial design features. For the traditional deep learning model of vegetable disease image recognition, the model recognition accuracy can be improved by increasing the network level. However, as the network level increases to a certain depth, it will lead to the degradation / disappearance of the network gradient, which degrades the recognition performance of the learning model. Therefore, a method of vegetable disease identification based on deep residual network model was studied in this paper. Firstly, considering that the super parameter value in the deep network model has a great influence on the accuracy of network identification, Bayesian optimization algorithm was used to autonomously learn the hyper-parameters such as regularization parameters, network width, stochastic momentum et al, which are difficult to determine in the network, eliminate the complexity of manual parameter adjustment, and reduce the difficulty of network training and saves the time of network construction. On this basis, the gradient could flow directly from the latter layer to the former layer through the identical activation function by adding residual elements to the traditional deep neural network. The deep residual recognition model takes the whole image as the input, and obtains the optimal feature through multi-layer convolution screening in the network, which not only avoids the interference of human factors, but also solves the problem of the performance degradation of the disease recognition model caused by the deep network, and realizes the high-dimensional feature extraction and effective disease recognition of the vegetable image. Relevant simulation results show that compared with other traditional models for vegetable disease identification, the deep residual neural network shows better stability, accuracy and robustness. The deep residual network model based on hyperparametric self-learning achievesd good recognition performance on the open data set of tomato diseases, and the recognition accuracy of 4 common diseases of tomato leaves reached more than 95%. The researth can provide a basic methed for fast and accurate recognition of tomato leaf diseases. Reference | Related Articles | Metrics | Comments（0）

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Effect of Downwash Airflow Field of 8-rotor Unmanned Aerial Vehicle on Spray Deposition Distribution Characteristics under Different Flight Parameters WANG Changling, HE Xiongkui, BONDS Jane, QI Peng, YANG Yi, GAO Wanlin Smart Agriculture    2020, 2 (4): 124-136.   DOI: 10.12133/j.smartag.2020.2.4.202003-SA005 Abstract （1158）   HTML （284）    PDF（pc） （2470KB）（1107）       Knowledge map    Save Pesticide application using UAV sprayer has become a new highlight in the development of agricultural machinery and plant protection in China. Spray droplets from UAV could reach the crop canopy and deposit on the control target surface under the assistance of rotor's downwash airflow after atomization, including a secondary atomization effect of airflow on the droplets, so the spray performance of aerial pesticide application is inseparable from the effect of the rotor's downwash airflow field. In order to explore the effect of downwash airflow field on UAV's spray deposition characteristics, taking the main model of eight-rotor UAV with "X-type" as the research object and designing the actual measuring test, a multi-channel micro-meteorology measurement system(MMMS) was used to determine the downwash airflow speed at different horizontal positions, and meanwhile the tracer Allura Red solution was applied instead of chemicals to obtain the distribution characteristics of spray deposition. The visual analysis of the measured results of the downwash airflow field distribution was focused, and then the distribution characteristics of both the downwash airflow field and the droplet deposition at a certain flight height and speed, and the correlation relationship between them were analyzed. During the flight operation of the 8-rotor UAV, as the flight speed increased from 1.0 to 6.0 m/s and the flight height increased from 1 to 2 m, the intensity of the downwash airflow field in directions of X, Y, and Z generally changed from strong to weak, and the distribution state changed from concentration to dispersion; the X direction airflow was the vortex generated by the interaction between the downwash airflow and the outside air and its effect on droplets was reversed flight direction; the airflow in Y direction was to the both sides from flight path, caused by the combination of downwash airflow and ground effect; the airflow in Z direction, the vertical downward component of the downwash airflow, had a direct promotion effect on spray deposition. Significant negative correlations were shown between both the flying speed and the peak value in the range of the downwash airflow field (P <0.05, r = -0.836), and the flying speed and the average deposition within the effective spray swath(P <0.05, r = -0.833). When the flight speed was 1.0 and 3.0 m/s, the droplet deposition showed a very significant positive correlation with downwash airflow speed(P <0.01, r> 0), that was, the stronger the downwash airflow field in the vertical ground direction, the more droplets deposited in the effective spray swath. When the flight speed increased to 6.0 m/s, the wind speed was significantly reduced, and the promotion effect of the downwash airflow field on the droplet deposition disappeared(P> 0.05). The operation speed of UAV should not be set faster than 6.0 m/s to avoid the chemicals loss caused by the weakened effect of the downwash airflow field. The findings of this study are expected to provide theoretical basis and data support for improving the quality of low-altitude and low-volume application operations and the formulation of UAV field operations specifications. Reference | Related Articles | Metrics | Comments（0）

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Automatic Weed Detection Method Based on Fusion of Multiple Image Processing Algorithms MIAO Zhonghua, YU Xiaoyao, XU Meihong, HE Chuangxin, LI Nan, SUN Teng Smart Agriculture    2020, 2 (4): 103-115.   DOI: 10.12133/j.smartag.2020.2.4.202010-SA006 Abstract （1301）   HTML （1550）    PDF（pc） （4725KB）（1106）       Knowledge map    Save Automatic weeding is a hot research field of smart agriculture, which has many benefits such as achieving precise weed control, saving human cost, and avoiding damage on crops, etc. Recently, many researchers have focused on the research using the deep learning method, such as the convolutional neural network (CNN) and recurrent neural network (RNN) and have achieved decent outcomes related to the automatic weed detection. However, there are still generally problems of the projects such as weak robustness and excessive reliance on a large number of samples. To solve these problems, a recognition algorithm for automatic identification and weed removal was designed, and a soybean field weed detection and localization method based on the fusion of multiple image processing methods was proposed in this study. The images and video stream were obtained through the camera mounted on a mobile robot platform. Firstly, the soil background inside the image was segmented from the foreground (including the weeds and crops) by setting the threshold for a specific color space (hue). Then, three different methods including the area threshold method, template matching and saturation threshold method were used to classify the crops and weeds. Finally, based on a proposed innovative voting method, the three recognition methods were comprehensively weighed and fused to achieve more accurate recognition and localization results of the crops and weeds inside the image. Experimental validations were carried out using the samples obtained through the moving platform, and the experimental results showed that the average accuracy of the proposed weed detection algorithm was as high as 98.21%, while the recognition error was only 1.79%. Meanwhile, compared with each single method as the scale threshold, template matching and saturation threshold, the fused method based on the weighted voting has been able to raise the average accuracy by 5.71%. Even though the samples used in the validations were limited in covering different scenarios, the high recognition accuracy has proved the practicability of the proposed method. In addition, the robustness test that images with raindrop and shadow interference in the complex and unstructured agricultural scene was carried out, and satisfied results showed that above 90% of the plant were successfully detected, which verified the fine adaptability and robustness of the proposed method. Reference | Related Articles | Metrics | Comments（0）

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Vision Servo Control Method and Tapping Experiment of Natural Rubber Tapping Robot ZHOU Hang, ZHANG Shunlu, ZHAI Yihao, WANG Song, ZHANG Chunlong, ZHANG Junxiong, LI Wei Smart Agriculture    2020, 2 (4): 56-64.   DOI: 10.12133/j.smartag.2020.2.4.202010-SA001 Abstract （1196）   HTML （238）    PDF（pc） （1923KB）（1103）       Knowledge map    Save Automated rubber tapping not only frees the workers from heavy physical labor and harsh working conditions, but also reduces the dependence on the workers' skills and greatly increases tapping efficiency. The key technologies for tapping robots are the independent acquisition of operational information and servo control of the tapping position in unstructured environments. In this study, taking rubber tree in rubber plantations as object, incorporating robot kinematics, machine vision technology and multi-sensor feedback control technology, a modular prototype of a rubber tapping robot was developed. The rubber tapping robot was mainly composed of an orbital mobile platform, a multi-joint robotic arm, a binocular stereo vision system and an end-effector. The binocular stereo vision and structured light system were used to obtain the structural parameters of the rubber trunk and secant. A six-joint tandem robotic arm was used for the planning and realization of complex rubber tapping trajectories. An multi-sensor fusion end-effector was developed to complete the identification of the starting point, the measurement of cut compensation and the tapping operation. To address the technical difficulties in rubber tapping operations, such as complex and variable environment, superimposed interaction of operational information, similar target background features and sub-millimeter operational accuracy requirements, the spatial mathematical model of the rubber tapping trajectory was established to plan the robot's movement path for fast approaching and moving away from the operation space. The results of the field tests conducted at a natural rubber plantation in Hainan province showed that the accuracy in bark consumption was about 0.28 mm and the accuracy in cutting depth was about 0.49 mm when the rubber tapping robot cut 1 mm thick bark. Compared to manual operations, the continuity of the chips and the flatness of the rubber output surface were improved significantly. This research could provide a positive reference and development direction for exploring automated rubber tapping operations. Reference | Related Articles | Metrics | Comments（0）

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An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform Liu Shouyang, Jin Shichao, Guo Qinghua, Zhu Yan, Baret Fred Smart Agriculture    2020, 2 (1): 87-98.   DOI: 10.12133/j.smartag.2020.2.1.202002-SA004 Abstract （1551）   HTML （1414）    PDF（pc） （1794KB）（1103）       Knowledge map    Save The capacity of canopy light interception is a key functional trait to distinguish the phenotypic variation over genotypes. High-throughput phenotyping canopy light interception in the field, therefore, would be of high interests for breeders to increase the efficiency of crop improvement. In this research, the Digital Plant Phenotyping Platform（D3P） was used to conduct in-silico phenotyping experiment with LiDAR scans over a wheat field. In this experiment virtual 3D wheat canopies were generated over 100 wheat genotypes for 5 growth stages, representing wide range of canopy structural variation. Accordingly, the actual value of traits targeted were calculated including GAI (green area index), AIA (average inclination angle) and FIPARdif (the fraction of intercepted diffuse photosynthetically activate radiation). Then, virtual LiDAR scanning were accomplished over all the treatments and exported as 3D point cloud. Two types of features were extracted from point cloud, including height quantiles (H) and green fractions (GF). Finally, an artificial neural network was trained to predict the traits targeted from different combinations of LiDAR features. Results show that the prediction accuracy varies with the selection of input features, following the rank as GF + H > H > GF. Regarding the three traits, we achieved satisfactory accuracy for FIPARdif (R2=0.95) and GAI (R2=0.98) but not for AIA (R2=0.20). This highlights the importance of H feature with respect to the prediction accuracy. The results achieved here are based on in-silico experiments, further evaluation with field measurement would be necessary. Nontheless, as proof of concept, this work further demonstrates that D3P could greatly facilitate the algorithm development. Morever, it highlights the potential of LiDAR measurement in the high-throuhgput phenopyting of canopy light interpcetion and structural traits in the field. Reference | Related Articles | Metrics | Comments（0）

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Analysis of spatial pattern and ecological service value changes of large-scale regional paddy fields based on remote sensing data Liu Yuan, Zhou Qingbo, Yu Qiangyi, Wu Wenbin Smart Agriculture    2020, 2 (1): 43-57.   DOI: 10.12133/j.smartag.2020.2.1.202001-SA002 Abstract （836）   HTML （1363）    PDF（pc） （2131KB）（1099）       Knowledge map    Save Under the pressure of economy development and climate change, rice production and distribution in the Yangtze River basin have undergone great changes, which may pose a great threat to the ecological environment and food security. Based on land use remote sensing-monitoring data from1990 to 2015, the GIS spatial analysis method was used to explore the spatial pattern variation characteristics of paddy fields in the Yangtze River economic belt. Meanwhile, Ecosystem services value (ESV) was calculated by using the equivalent factor method corrected by region and time factor to measure the comprehensive impact of paddy field change. The results showed that, to begin with, paddy fields number of the Yangtze River economic belt continued to decrease, with a total decrease of 17 390km2, and the decrease rate presented a trend of growth with significant regional differences. The difference between the reduction rate of paddy fields in the middle upper and the lower reaches of the Yangtze River was about 9.56%. Among them, in the lower reaches of the Yangtze River, the proportion of paddy fields decreased, while in the middle and upper reaches which was just the opposite. Then, paddy fields mainly flowed to construction land and water, resulting from economic construction and aquaculture development. Paddy fields chiefly came from water, dry land and wetland, etc. Furthermore, paddy fields in the Yangtze River Delta, the middle reaches of the Yangtze River and the Chengdu-Chongqing urban agglomerations changed the most dramatically. The expansion of construction land to paddy fields was widespread, and paddy fields flooded by water primarily distributed in the two lake plains. In addition, the conversion of paddy fields and other ecosystems had a positive impact on ESV, in which the paddy-water diversion type contributed the most. Its scale determined the net increase of ESV in different periods. Value loss lead by conversion from water to paddy fields was the largest, and construction land invading paddy fields was the second. The conversions in different cities were different, so the difference in ESV increases and decreases. In addition, the tradeoffs within ecosystem services were mainly between hydrological regulation, water supply and food production, gas regulation, which were directly related to the increase of water resources and the loss of paddy fields. The research results are helpful to reveal the spatio-temporal changes process of paddy fields in the Yangtze River basin and its impact on ecological functions, and provide theoretical support for regional land use planning, agricultural policy and ecological sustainable development. Reference | Related Articles | Metrics | Comments（0）

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Developmental model of wheat smart production based on the integration of information technology, agricultural machinery and agronomy Ma Xinming, Ma Zhaowu, Xu Xin, Xi Lei, Xiong Shuping, Li Haiyang Smart Agriculture    2019, 1 (4): 62-71.   DOI: 10.12133/j.smartag.2019.1.4.201910-SA001 Abstract （1191）   HTML （1574）    PDF（pc） （1015KB）（1090）       Knowledge map    Save In order to study the development mode and realization way of smart agriculture, the technical route of agricultural information fusion of agricultural machinery in different production stages before, during and after wheat production was designed. Pre-production: use Beidou precision navigation technology and motion planning optimization method to realize the full area coverage path planning of the field operation of the automatic navigation tractor, combine the laser leveling equipment to realize the accurate and standardized land leveling and laser leveling, and realize the accurate and standardized operation of the land. On this basis, the spatial interpolation technology was used to make the variable fertilization prescription map and combining variable rate fertilizer machine and realized variable rate precise application of fertilizer and precise seeding. At the same time, combining with the optimal design of planting scheme, based on the prenatal database and knowledge base, it optimizes the decision-making of variety configuration and sowing time and seeding amount were optimized, and the software intelligent decision-making technology was used to recommend the varieties and sowing time and seeding amount suitable for planting at the decision-making point, and constructs the wheat and maize prenatal information service recommendation system based on WebGIS was constructed. In production: based on the image technology of automatic segmentation and color feature extraction of wheat image in the field environment, a remote monitoring model of wheat nutritional status with the function of wheat population image segmentation and nutritional estimation was established to realize the non-destructive monitoring of wheat nutritional status in the field environment. After production, the integrated measurement sensor, speed sensor, header height sensor and GPS were adopted, and controller area network bus was adopted with wireless communication technology, a real-time wheat yield measurement system was developed, which was installed on a large-scale combine harvester to carry out the real-time prediction service of wheat yield, so as to realize the synchronous process of wheat harvest and yield measurement, with the error less than 5%. The intelligent transformation of common agricultural machinery equipment and the research and development of sowing and harvesting equipment adapted to agricultural production were completed and realized, and the small scale with high-efficiency utilization of light and heat resources, increase of output and green development were studied. The model of wheat planting production was optimized .A real time measurement and prediction system for postpartum yield was developed, which included the selection of sowing date, fertilization recommendation, seedling growth and nutrition diagnosis. The experimental results show that the adoption of agricultural information fusion technology can increase wheat yield by 18.4%, input-output ratio by 16.6% and 7.9%, which shows that the intelligent agriculture of Henan province is effective and feasible. Reference | Related Articles | Metrics | Comments（0）

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Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean CHEN Ailian, LI Jiayu, ZHANG Shengjun, ZHU Yuxia, ZHAO Sijian, SUN Wei, ZHANG Qiao Smart Agriculture    2020, 2 (3): 139-152.   DOI: 10.12133/j.smartag.2020.2.3.202006-SA002 Abstract （1157）   HTML （805）    PDF（pc） （4211KB）（1089）       Knowledge map    Save In recent years, revenue protection crop insurance is an innovative insurance that has been prioritized in China. But it still lacks the support of the third-party yield data around crop harvest time. Aiming to provide objective yield data for revenue protection crop insurance, satellite remote sensing production estimation technology was employed to discuss its application mode and applicability. Taking the soybean revenue protection insurance in Jiaxiang county, Shandong province as an example, we first extracted soybean planting plots, calculated vegetation index and crop physiological parameters based on Sentinel-2 satellite images in 2018 . Combining to TRMM precipitation data from TRMM precipitation-monitoring radar satellite and MODIS land surface temperature data from Terra/Aqua satellite and site yield data, we established a multi-parameter linear regression model, and estimated soybean yield per unit area. The crop extraction results showed that the soybean planting area in the study area was 1.24 km2, which was in good agreement with the 1.27 km2 reported by the local agricultural bureau; and with using the actual measurement plots, the remote sensing identification accuracy of the planting distribution plots reached 90%. The yield estimation results showed that the NDVI of the soybean pod stage on August 23 and the leaf area index of the soybean seedling stage on September 7 explained the soybean yield per hectare the best, and the average estimated yield of the whole area was 244,500 kg/m2, which reflects the severely affected agricultural conditions, comparing to 299,800 kg/km2 in previous years.The regression coefficient between the estimated yield data and the measured data reached 0.92, which meet the application needs.With this results, the estimated yield of different towns can be summarized, and the regional yield was present, and was used as the real yield in 2018, multiplying with the average soybean price around October 11 to December 10 from the local price bureau, the real revenue was obtained. Compared the real revenue to the expected revenue in the contract of insurance, the claims work was decided. The results indicated that the Sentinel-2 satellite data could be used to identify the soybean planting distribution in the study area accurately, and to complete the yield estimation as soon as one week after the soybean harvest, which could guide the insurance company's claims work. The whole methodology is capable of aiding the claims work in revenue protection crop insurance. Reference | Related Articles | Metrics | Comments（0）

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Energy optimization strategy for wireless sensor networks in large-scale farmland habitat monitoring Zhang Xiaohan, Yin Changchuan, Wu Huarui Smart Agriculture    2019, 1 (2): 55-63.   DOI: 10.12133/j.smartag.2019.1.2.201812-SA024 Abstract （1014）   HTML （314）    PDF（pc） （1207KB）（1088）       Knowledge map    Save Wireless sensor network (WSN) has been widely deployed in precision agriculture to improve the crop production. However, we still face many challenges for large-scale farmland habitat monitoring, the energy shortage for battery-powered sensor nodes, the complicated propagation environment for wireless signals during different growing-up periods of the crops, the optimization of the coverage of the sensor nodes, etc. In order to guarantee the holeless coverage of the sensor nodes during the whole life of the crops, the WSNs are typically deployed with high density. Therefore, some of the sensors in the network are redundant in certain growing-up periods of the crops. And also the data collected by each sensor node may have strong temporal correlation. Recently, compressive sensing (CS) has received much attention due to its capability of reconstructing sparse signals with the number of measurements much lower than that of the Nyquist sampling rate. With the rapid progress of CS based sparse representation, matrix completion (MC) theory was proposed very recently. According to the MC theory, a low-rank matrix can be accurately rebuilt with a few number of entries in the matrix. Matrix completion provides the advantage of sampling small set of data at sensor nodes without requiring excessive computational and traffic loads, which meets the requirement of energy-efficient data gathering and transmitting in WSNs. In this research, by considering the characteristics that the sensor nodes are redundant in some growing-up periods of the crops and the data collected by sensor-nodes usually share a strong spatial and temporal correlation among them in WSNs for large-scale farmland habitat monitoring, we put forward a MC based two-step energy saving optimization algorithm to reduce both the energy consumption of the data acquisition and data transmission process in WSNs and achieved the purpose of prolonging the network lifetime. Firstly, through the measurement of the sensor node's data information, we found the non-redundant nodes by considering the spatial correlation of the data from the sensor nodes. We would close the data acquisition units of the remaining redundant nodes and make them only transmit data as relay nodes. Secondly, we took advantage of the partial sampling scheme in matrix completion to further reduce the quantity of data. Thereby, we could reduce the energy consumption on both data collection and transmission process of the wireless sensor network. The experiment results show that the proposed algorithm reduces 83% working nodes in the network, and therefore reducing the energy cost of the network. Reference | Related Articles | Metrics | Comments（0）

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Edge extraction method of remote sensing UAV terrace image based on topographic feature Yang Yanan, Kang Yang, Fan Xiao, Chang Yadong, Zhang Hanwen, Zhang Hongming Smart Agriculture    2019, 1 (4): 50-61.   DOI: 10.12133/j.smartag.2019.1.4.201908-SA005 Abstract （1130）   HTML （1009）    PDF（pc） （2234KB）（1085）       Knowledge map    Save Terraces achieve water storage and sediment function by slowing down the slope and soil erosion. This kind of terraced or wave-section farmland built along the contour line on is a high-yield and stable farmland facility with key construction in the dry farming area. It provides a strong guarantee for increasing grain production and farmers' income. In recent years, Gansu province has carried out a large amount of construction on terraces, however, due to the poor quality of the previous construction and management, the terraced facilities are in danger of being destroyed. In order to prevent and repair the terraces, it is necessary to timely and accurately extract the terrace information. The segmentation of terraces can be obtained by edge extraction, but the effect of satellite data is not ideal. With the continuous development of remote sensing technology of drones, the acquisition of high-precision terrace topographic information has become possible. In this research, the slope is extracted from the digital elevation model data in the data preprocessing stage, then the orthophoto data of the three experimental areas are merged with the corresponding slope data, respectively. Then the rough edge extraction method based on Canny operator and the fine edge extraction method based on multi-scale segmentation are used to perform edge detection on two data sources. Finally, the influence of slope on the extraction of terraced edges of remote sensing images of UAVs was analyzed based on the overall accuracy of edge detection and user accuracy. The experimental results showed that, in the rough edge extraction method, the data source accuracy of the fusion slope and image was improved by 23.97% in the OA precision evaluation, and the average improvement in the UV accuracy was 20.68%. In the fine edge extraction method, the accuracy based on the data source 2 was also increased by 17.84% on average in the OA accuracy evaluation of the data source 1, and by an average of 19.0% in the UV accuracy evaluation. The research shows that in the extraction of terraced edges of UAV remote sensing images, adding certain terrain features can achieve better edge extraction results. 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 （674）   HTML （74）    PDF（pc） （1057KB）（1072）       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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Design and Prospect for Anti-theft and Anti-destruction of Nodes in Solar Insecticidal Lamps Internet of Things HUANG Kai, SHU Lei, LI Kailiang, YANG Xing, ZHU Yan, WANG Xiaochan, SU Qin Smart Agriculture    2021, 3 (1): 129-143.   DOI: 10.12133/j.smartag.2021.3.1.202102-SA034 Abstract （1237）   HTML （91）    PDF（pc） （2413KB）（1071）       Knowledge map    Save Solar insecticidal lamps (SILs) are widely used in agriculture for the purpose of effectively controlling pests and reducing pesticide dosage. With the increasing deployment of SILs, there are more and more reports about theft and destruction of SILs, seriously affecting the pest control effect and leading to great economic losses. Unfortunately, many efforts remain unsuccessful, since people can destruct the components of SIL in part but not steal the whole SIL, which cannot be detected by GPRS module or can only be labeled as a fault of component. To realize the broader effect of anti-theft and anti-destruction in the scenario of Solar Insecticidal Lamps Internet of Things (SIL-IoTs), there were two types of designs which would enable substantial improvements. On one hand, SIL was reformed and designed to obtain more information from different kinds of sensors and increase the difficulty of theft and destruction of SILs. Four modules were equipped including gated switch, voltage and current module, emergency power module, acceleration sensor module. Gated switch was used to judge whether the gate of power was open or closed. Voltage and current module of battery, solar panel, lamp, and metal mesh were used to judge whether the components were stolen or destructed. Emergency power module was used for communication module after the battery being stolen. Acceleration sensor module was used to judge whether the SIL was shaking by stealer. On the other hand, the auxiliary equipment of SIL, i.e., unmanned aerial vehicle insecticidal lamp (UAV-IL), was put forward for emergency applications after theft and destruction of SIL, e.g., deployment, tracking, patrol inspection, and so on. Through the above-mentioned hardware design and application of UAV-IL, more information from different kinds of sensors could be obtained to make judgements about the situation of theft and destruction. However, considering the short occurrence time of theft and destruction, the design was not enough to realize fast and accurate judgments. Therefore, six key research issues in the design of internal hardware, software algorithm and appearance structure design level were discussed, including 1) optimal design of anti-theft and anti-destruction of SILs; 2) establishment of anti-theft and anti-destruction judgment rules of SILs; 3) fast and accurate judgments of theft and destruction of SILs; 4) emergency measures after theft and destruction of SILs; and 5) prediction and prevention of theft and destruction of SILs; 6) optimal calculation to reduce the load of network data transmission. The anti-theft and anti-destruction have crucial roles in equipment safety, which can be extended to various agricultural applications. Reference | Related Articles | Metrics | Comments（0）

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Monitoring Specified Depth Soil Moisture in Field Scale with Ground Penetrating Radar ZHANG Wenhan, DU Keming, SUN Yankun, LIU Buchun, SUN Zhongfu, MA Juncheng, ZHENG Feixiang Smart Agriculture    2022, 4 (1): 84-96.   DOI: 10.12133/j.smartag.SA202202010 Abstract （706）   HTML （86）    PDF（pc） （2258KB）（1071）       Knowledge map    Save Ground-penetrating radar (GPR) is one of the emerging technologies for soil moisture measurement. However, the measurement accuracy is difficult to determine due to some influence factors including radar wave frequency, soil texture type, etc. The GPR equipment with 1000 MHz center frequency and the measurement method of common midpoint (CMP) were adopted in the research to collect radar wave raw data in the selected field area under arid soil and moist soil conditions. The transmitter and receiver antennas of the GPR equipment were moved 0.01 m respectively in opposite directions on each radar wave raw data collection. Therefore, a CMP radar image consisted of 100 pieces of radar wave raw data by increasing the antenna distance from 0 m to 2 m. Each radar wave raw data indicated that the radar waves were reflected in the reflective layer with different dielectric constant under the same antenna distance. And the reflected and refracted radar waves were acquired by the receiving antenna at different two-way travel time respectively, and recorded in the computer. The collection of CMP soundings aimed to determine the inversion accuracy, optimum inversion depth, effective inversion depth and optimal inversion model of soil moisture content at different depth ranges and adjacent reflective layers by GPR at field scale. The reflected and refracted radar wave data were extracted from the raw data. The velocities of the surface waves and reflected waves were obtained respectively from the line slope of the surface wave data and the hyperbolic curves fitting of the reflected wave data. In addition, the relative dielectric constant of the soil at specified depth were deduced according to the soil dielectric constant and its reflected wave velocity. Moreover, 4 different models including Topp, Roth, Herkelrath and Ferre were used to figure out the soil volumetric water content inversion. Meanwhile, the measured data of soil volumetric moisture content obtained by oven drying method were used to verify the accuracy of the inversion results. The results showed that the effective inversion depth of 1000 MHz GPR ranged from 0 to 50 cm. The best inversion depth was 50 cm in arid soil and 40 cm in moist soil. The Roth model had the best correlation and stability with the highest R2 was 0.750, the Root Mean Square Error (RMSE) was 0.0114 m3/m3 and the lowest Relative Error (RE) was 3.0%. The GPR could possess the capacity of quick, precise and non-destructive measurement of specified depth soil moisture in field scale. The inversion model of soil moisture content needs to be calibrated according to different soil conditions. Reference | Related Articles | Metrics | Comments（0）

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Evaluation of Droplet Size and Drift Distribution of Herbicide Sprayed by Plant Protection Unmanned Aerial Vehicle in Winter Wheat Field WANG Guobin, HAN Xin, SONG Cancan, YI Lili, LU Wenxia, LAN Yubin Smart Agriculture    2021, 3 (3): 38-51.   DOI: 10.12133/j.smartag.2021.3.3.202107-SA005 Abstract （767）   HTML （31）    PDF（pc） （2496KB）（1069）       Knowledge map    Save With the continuous increase of the spraying area, the problem of droplet drift risk in the spraying process of UAV is becoming increasingly prominent, especially the herbicide drift. In order to clarify the effect of the herbicide solution on the droplet size and the deposition and drift distribution characteristics sprayed by UAVs, the droplet sizes of 15 herbicide solutions sprayed by the centrifugal rotary atomizer nozzle installed in the plant protection UAV were measured in the laboratory, and the distribution of droplet deposition and drift in the spraying area and drift area were measured by adding a fluorescent tracer (60 g/hm2) to the tank in the field. The results showed that the herbicide solution had a significant effect on the droplet size distribution. The DV50 of all the other solutions was reduced after sprayed by the centrifugal atomizer except the Carfentrazone-ethyl water dispersible granule, and the maximum decrease ratio was 22.0%. The proportion of small droplets (V<150 μm) increased, with the maximum value of 50.8%. When the environmental crosswind speed was 3.76 m/s, the coverage and number of droplets in the spraying area were only 41.3% and 42.2% of that at 0.74 m/s, and the deposition uniformity was significantly reduced. In the drift zone, the deposition amount of droplets was under 10% of in-swath zone at the downwind of 12 m, and the deposition of all the treatments at 50 m was lower than detection limits (0.0002 μL/cm2). The drift ratio increased with the wind speed increased. When the crosswind speed reached 3.76 m/s, the drift ratio of droplets was 46.4%. Under different crosswind, 90% of the total measured spray drift were 4.8?22.4 m. By fitting the deposition in the drift zone with drift distance and crosswind speed, the downwind deposition was proportional to the crosswind speed. This study provides data support for droplet drift distance of plant protection UAV spraying in wheat fields at different wind speeds in winter and provides a basis for spray drift buffer zone, drift risk assessment, and relevant standard formulation. Reference | Related Articles | Metrics | Comments（0）

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Design and test of wheat stripe rust remote monitoring platform based on embedded system Ji Yunzhou, Du Shengjia, Ji Tongkui, Song Huaibo Smart Agriculture    2019, 1 (3): 100-112.   DOI: 10.12133/j.smartag.2019.1.3.201903-SA004 Abstract （1119）   HTML （310）    PDF（pc） （5092KB）（1068）       Knowledge map    Save Wheat stripe rust is an important biological disaster that affects the safe production of wheat in China for a long time. The number of spores of wheat stripe rust is a direct factor affecting its pathogenesis and transmission. At present, it mainly relies on the field sampling and investigation of agricultural technicians to predict and forecast. It is time-consuming and laborious, and difficult to achieve long-term monitoring of diseases, thus affecting the accuracy of forecasting and the timeliness of prevention and control. The existing automatic spore monitoring device also has the problems that the collecting device is mostly in the form of manual replacement of slides, and the direct acquisition of components in the air by a limited area of the slide may result in inaccurate sample collection and too small sample size. In order to further improve the monitoring and forecasting ability of wheat stripe rust, a wheat stripe rust monitoring device was designed and implemented, which based on the internet to build a wheat stripe rust monitoring platform, and based on the embedded system to establish a complete set of wheat stripe rust spore collection and image transmission processing device. Spore acquisition was performed using a slide adsorption device of "Six prism column + Electromagnet + Microscope". Control the up and down movement of the electromagnet to control the up and down movement of the slide; update the slide by controlling the rotation of the hexagonal shaft; obtain the image by controlling the time synchronization of the microscope and the shaft; control the cleaning solvent the smear and the movement of the cleaning block enable the slide to be cleaned. At the same time, a spore counting program based on the server platform was designed to process and analyze the collected slide images. The spore counting program used in this design is based on Python 3.6 and combined with the Skimage image processing package for spore image analysis and processing. The geometry factor feature based method was used, and the number of spores in the microscope field was finally obtained based on the regional attribute values. The experimental results show that the platform server image processing algorithm can achieve accurate counting of spores, the accuracy of counting the test images is 100%; the success rate of the slide switching system is 95%.This study can lay a foundation for the real-time monitoring of wheat stripe rust in the field, and can also provide references for the monitoring of other airborne diseases in the field. 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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Design and Application of Facility Greenhouse Image Collecting and Environmental Data Monitoring Robot System GUO Wei, WU Huarui, ZHU Huaji Smart Agriculture    2020, 2 (3): 48-60.   DOI: 10.12133/j.smartag.2020.2.3.202007-SA006 Abstract （1199）   HTML （1984）    PDF（pc） （2668KB）（1040）       Knowledge map    Save China's facility horticulture has developed rapidly in the past 30 years and now comes to the first in the world in terms of area. However, the number of farmers is decreasing. "Machine replaces labor" has become the current research hotspot. In order to realize the fine collection of crop images and environmental monitoring data, a three-dimensional environmental robot monitoring system for crops was designed. The robot consists of three parts: perception center, decision center and execution center, which carry out environmental perception from machine perspective, data analysis, decision instruction generation and action execution respectively. In perception layer, the system realized real-time videos, images, data monitoring such as air temperature, air humidity, illumination intensity and concentrations of carbon dioxide in grid scale from multi-angle with high accuracy. At the system level, automatic speech recognition was integrated to make the system easier to use, especially for farmers who usually work in the fields. In transport layer, monitoring data and control instructions were converged to local data center through wireless bridges. Concretely, transmission mode was chosen according to different characteristics of data, wire transmission is available for big size data, such as images and videos, while wireless transmission is mainly applied to small size data, such as environmental monitoring parameters. In data processing layer, feedbacks and control instructions were made by multi-source heterogeneous data of crop model analysis, in terms of commands, independent inspection mode and real-time remote-control mode were available for users. Plant type, user information, historical data and management data were taken into consideration. Finally, in application layer, the system provided web and mobile intelligence services that could be used for the whole growth periods in terms of images, real-time videos, monitoring data collection and analysis of cucumbers, tomatoes, greenhouse peaches, etc. The system has been demonstrated and applied in solar greenhouse No. 7 of Beijing Xiaotangshan National Precision Agriculture Base and No. 5 of Shijiazhuang Agricultural and Forestry Science Research Institute with good achievements. Farmers and researchers have realized real-time monitoring, remote control and management. On one hand, the system can used to avoid working in extreme environment, such as high temperature and pesticide environment. On the other hand, with the help of the robot, independent inspection and data collection could achieve instead of people, and it is very intuitive in time-saving and indirect costs saving for productions and researchers. The results showed that the system could be widely applied in greenhouse facilities production and research. Reference | Related Articles | Metrics | Comments（0）

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Rapid detection of citrus Huanglongbing using Raman spectroscopy and Auto-fluorescence spectroscopy Dai Fen, Qiu Zeyuan, Qiu Qian, Liu Chujian, Huang Guozeng, Huang Yalin, Deng Xiaoling Smart Agriculture    2019, 1 (3): 77-86.   DOI: 10.12133/j.smartag.2019.1.3.201812-SA026 Abstract （1225）   HTML （80）    PDF（pc） （4597KB）（1026）       Knowledge map    Save In order to detect citrus Huanglongbing (HLB, also named citrus greening) quickly, Auto-fluorescence and Raman spectra of HLB leaf samples and healthy ones were collected and analyzed. PLS-DA models based on Auto-fluorescence spectra, Raman spectra and mixed spectra were established and compared respectively. Finally, ROC curves of the three models were drawn, and the performance of the models were further evaluated by using the area under curve AUC parameters. The results demonstrated spectral differences between Huanglongbing samples and healthy ones could be seen. With 785 nm laser irradiation, citrus leaf samples produced strong Auto-fluorescence and Raman peaks. The Auto-fluorescence of HLB leaves was weaker than that of healthy samples in the range of 800-1203 cm -1, but stronger in the range of 1206-1800 cm -1, and the slope of decline (absolute value) was smaller than that of healthy samples. The similar shapes were found in the Raman spectra of typical HLB samples and healthy ones. But the HLB samples had larger Raman peak intensity and spectral bandwidth at 1257 cm -1, 1396 cm -1, 1446 cm -1, 1601 cm -1 and 1622 cm -1 than healthy ones. The Raman peak intensity of HLB samples was weaker than that of healthy samples at 1006 cm -1, 1160 cm -1, 1191 cm -1 and 1529 cm -1 positions, suggesting that the carotenoid content of HLB samples was lower than healthy ones. The Auto-fluorescence model, the Raman spectral model and the mixed spectral model could distinguish two kinds of samples with the accuracy of 86.08%, 98.17% and 94.75%, respectively. Furthermore, AUCs of Receiver Operating Characteristic Curve (ROC) were calculated. The AUCs for the Auto-fluorescence model, the Raman spectral model and the mixed spectral model were0.9313、0.9991 and 0.9875, respectively. Through further analysis of ROC curve, the identification effect of the Raman spectral model was optimal. Raman spectroscopy could be a new way to explore the rapid diagnosis of citrus HLB. Reference | Related Articles | Metrics | Comments（0）