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Agri-QA Net: Multimodal Fusion Large Language Model Architecture for Crop Knowledge Question-Answering System WU Huarui, ZHAO Chunjiang, LI Jingchen Smart Agriculture    2025, 7 (1): 1-10.   DOI: 10.12133/j.smartag.SA202411005 Abstract （334）   HTML （74）    PDF（pc） （1010KB）（288）       Knowledge map    Save [Objective] As agriculture increasingly relies on technological innovations to boost productivity and ensure sustainability, farmers need efficient and accurate tools to aid their decision-making processes. A key challenge in this context is the retrieval of specialized agricultural knowledge, which can be complex and diverse in nature. Traditional agricultural knowledge retrieval systems have often been limited by the modalities they utilize (e.g., text or images alone), which restricts their effectiveness in addressing the wide range of queries farmers face. To address this challenge, a specialized multimodal question-answering system tailored for cabbage cultivation was proposed. The system, named Agri-QA Net, integrates multimodal data to enhance the accuracy and applicability of agricultural knowledge retrieval. By incorporating diverse data modalities, Agri-QA Net aims to provide a holistic approach to agricultural knowledge retrieval, enabling farmers to interact with the system using multiple types of input, ranging from spoken queries to images of crop conditions. By doing so, it helps address the complexity of real-world agricultural environments and improves the accessibility of relevant information. [Methods] The architecture of Agri-QA Net was built upon the integration of multiple data modalities, including textual, auditory, and visual data. This multifaceted approach enables the system to develop a comprehensive understanding of agricultural knowledge, allowed the system to learn from a wide array of sources, enhancing its robustness and generalizability. The system incorporated state-of-the-art deep learning models, each designed to handle one specific type of data. Bidirectional Encoder Representations from Transformers (BERT)'s bidirectional attention mechanism allowed the model to understand the context of each word in a given sentence, significantly improving its ability to comprehend complex agricultural terminology and specialized concepts. The system also incorporated acoustic models for processing audio inputs. These models analyzed the spoken queries from farmers, allowing the system to understand natural language inputs even in noisy, non-ideal environments, which was a common challenge in real-world agricultural settings. Additionally, convolutional neural networks (CNNs) were employed to process images from various stages of cabbage growth. CNNs were highly effective in capturing spatial hierarchies in images, making them well-suited for tasks such as identifying pests, diseases, or growth abnormalities in cabbage crops. These features were subsequently fused in a Transformer-based fusion layer, which served as the core of the Agri-QA Net architecture. The fusion process ensured that each modality—text, audio, and image—contributes effectively to the final model's understanding of a given query. This allowed the system to provide more nuanced answers to complex agricultural questions, such as identifying specific crop diseases or determining the optimal irrigation schedules for cabbage crops. In addition to the fusion layer, cross-modal attention mechanisms and domain-adaptive techniques were incorporated to refine the model's ability to understand and apply specialized agricultural knowledge. The cross-modal attention mechanism facilitated dynamic interactions between the text, audio, and image data, ensuring that the model paid attention to the most relevant features from each modality. Domain-adaptive techniques further enhanced the system's performance by tailoring it to specific agricultural contexts, such as cabbage farming, pest control, or irrigation management. [Results and Discussions] The experimental evaluations demonstrated that Agri-QA Net outperforms traditional single-modal or simple multimodal models in agricultural knowledge tasks. With the support of multimodal inputs, the system achieved an accuracy rate of 89.5%, a precision rate of 87.9%, a recall rate of 91.3%, and an F1-Score of 89.6%, all of which are significantly higher than those of single-modality models. The integration of multimodal data significantly enhanced the system's capacity to understand complex agricultural queries, providing more precise and context-aware answers. The addition of cross-modal attention mechanisms enabled for more nuanced and dynamic interaction between the text, audio, and image data, which in turn improved the model's understanding of ambiguous or context-dependent queries, such as disease diagnosis or crop management. Furthermore, the domain-adaptive technique enabled the system to focus on specific agricultural terminology and concepts, thereby enhancing its performance in specialized tasks like cabbage cultivation and pest control. The case studies presented further validated the system's ability to assist farmers by providing actionable, domain-specific answers to questions, demonstrating its practical application in real-world agricultural scenarios. [Conclusions] The proposed Agri-QA Net framework is an effective solution for addressing agricultural knowledge questions, especially in the domain of cabbage cultivation. By integrating multimodal data and leveraging advanced deep learning techniques, the system demonstrates a high level of accuracy and adaptability. This study not only highlights the potential of multimodal fusion in agriculture but also paves the way for future developments in intelligent systems designed to support precision farming. Further work will focus on enhancing the model's performance by expanding the dataset to include more diverse agricultural scenarios, refining the handling of dialectical variations in audio inputs, and improving the system's ability to detect rare crop diseases. The ultimate goal is to contribute to the modernization of agricultural practices, offering farmers more reliable and effective tools to solve the challenges in crop management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Artificial Intelligence-Driven High-Quality Development of New-Quality Productivity in Animal Husbandry: Restraining Factors, Generation Logic and Promotion Paths LIU Jifang, ZHOU Xiangyang, LI Min, HAN Shuqing, GUO Leifeng, CHI Liang, YANG Lu, WU Jianzhai Smart Agriculture    2025, 7 (1): 165-177.   DOI: 10.12133/j.smartag.SA202407010 Abstract （1059）   HTML （15）    PDF（pc） （1692KB）（193）       Knowledge map    Save [Significance] Developing new-quality productivity is of great significance for promoting high-quality development of animal husbandry. However, there is currently limited research on new-quality productivity in animal husbandry, and there is a lack of in-depth analysis on its connotation, characteristics, constraints, and promotion path. [Progress] This article conducts a systematic study on the high-quality development of animal husbandry productivity driven by artificial intelligence. The new-quality productivity of animal husbandry is led by cutting-edge technological innovations such as biotechnology, information technology, and green technology, with digitalization, greening, and ecologicalization as the direction of industrial upgrading. Its basic connotation is manifested as higher quality workers, more advanced labor materials, and a wider range of labor objects. Compared with traditional productivity, the new-quality productivity of animal husbandry is an advanced productivity guided by technological innovation, new development concepts, and centered on the improvement of total factor productivity. It has significant characteristics of high production efficiency, good industrial benefits, and strong sustainable development capabilities. China's new-quality productivity in animal husbandry has a good foundation for development, but it also faces constraints such as insufficient innovation in animal husbandry breeding technology, weak core competitiveness, low mechanization rate of animal husbandry, weak independent research and development capabilities of intelligent equipment, urgent demand for "machine replacement", shortcomings in the quantity and quality of animal husbandry talents, low degree of scale of animal husbandry, and limited level of intelligent management. Artificial intelligence in animal husbandry can be widely used in environmental control, precision feeding, health monitoring and disease prevention and control, supply chain optimization and other fields. Artificial intelligence, through revolutionary breakthroughs in animal husbandry technology represented by digital technology, innovative allocation of productivity factors in animal husbandry linked by data elements, and innovative allocation of productivity factors in animal husbandry adapted to the digital economy, has given birth to new-quality productivity in animal husbandry and empowered the high-quality development of animal husbandry. [Conclusions and Prospects] This article proposes a path to promote the development of new-quality productivity in animal husbandry by improving the institutional mechanism of artificial intelligence to promote the development of modern animal husbandry industry, strengthening the application of artificial intelligence in animal husbandry technology innovation and promotion, and improving the management level of artificial intelligence in the entire industry chain of animal husbandry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan Smart Agriculture    2024, 6 (6): 1-22.   DOI: 10.12133/j.smartag.SA202410018 Abstract （703）   HTML （124）    PDF（pc） （3047KB）（1634）       Knowledge map    Save [Significance] Rice smart unmanned farm is the core component of smart agriculture, and it is a key path to realize the modernization of rice production and promote the high-quality development of agriculture. Leveraging advanced information technologies such as the Internet of Things (IoT) and artificial intelligence (AI), these farms enable deep integration of data-driven decision making and intelligent machines. This integration creates an unmanned production system that covers the entire process from planting and managing rice crops to harvesting, greatly improving the efficiency and precision of rice cultivation. [Progress] This paper systematically sorted out the key technologies of rice smart unmanned farms in the three main links of pre-production, production and post-production, and the key technologies of pre-production mainly include the construction of high-standard farmland, unmanned nursery, land leveling, and soil nutrient testing. The construction of high-standard farmland is the foundation of the physical environment of the smart unmanned farms of rice, which provides perfect operating environment for the operation of modernized smart farm machinery through the reasonable layout of the field roads, good drainage and irrigation systems, and the scientific planting structure. Agricultural machine operation provides a perfect operating environment. The technical level of unmanned nursery directly determines the quality of rice cultivation and harvesting in the later stage, and a variety of rice seeding machines and nursery plate setting machines have been put into use. Land leveling technology can improve the growing environment of rice and increase the land utilization rate, and the current land leveling technology through digital sensing and path planning technology, which improves the operational efficiency and reduces the production cost at the same time. Soil nutrient detection technology is mainly detected by electrochemical analysis and spectral analysis, but both methods have their advantages and disadvantages, how to integrate the two methods to achieve an all-round detection of soil nutrient content is the main direction of future research. The key technologies in production mainly include rice dry direct seeding, automated transplanting, precise variable fertilization, intelligent irrigation, field weed management, and disease diagnosis. Among them, the rice dry direct seeding technology requires the planter to have high precision and stability to ensure reasonable seeding depth and density. Automated rice transplanting technology mainly includes three ways: root washing seedling machine transplanting, blanket seedling machine transplanting, and potting blanket seedling machine transplanting; at present, the incidence of problems in the automated transplanting process should be further reduced, and the quality and efficiency of rice machine transplanting should be improved. Precision variable fertilization technology is mainly composed of three key technologies: information perception, prescription decision-making and precise operation, but there are still fewer cases of unmanned farms combining the three technologies, and in the future, the main research should be on the method of constructing the whole process operation system of variable fertilization. The smart irrigation system is based on the water demand of the whole life cycle of rice to realize adaptive irrigation control, and the current smart irrigation technology can automatically adjust the irrigation strategy through real-time monitoring of soil, climate and crop growth conditions to further improve irrigation efficiency and agricultural production benefits. The field weed management and disease diagnosis technology mainly recognizes rice weeds as well as diseases through deep learning and other methods, and combines them with precision application technology for prevention and intervention. Post-production key technologies mainly include rice yield estimation, unmanned harvesting, rice storage and processing quality testing. Rice yield estimation technology is mainly used to predict yield by combining multi-source data and algorithms, but there are still problems such as the difficulty of integrating multi-source data, which requires further research. In terms of unmanned aircraft harvesting technology, China's rice combine harvester market has tended to stabilize, and the safety of the harvester's autopilot should be further improved in the future. Rice storage and processing quality detection technology mainly utilizes spectral technology and machine vision technology to detect spectra and images, and future research can combine deep learning and multimodal fusion technology to improve the machine vision system's ability and adaptability to recognize the appearance characteristics of rice. [Conclusions and Prospects] This paper reviews the researches of the construction of intelligent unmanned rice farms at home and abroad in recent years, summarizes the main difficulties faced by the key technologies of unmanned farms in practical applications, analyzes the challenges encountered in the construction of smart unmanned farms, summarizes the roles and responsibilities of the government, enterprises, scientific research institutions, cooperatives and other subjects in promoting the construction of intelligent unmanned rice farms, and puts forward relevant suggestions. It provides certain support and development ideas for the construction of intelligent unmanned rice farms in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Prospects of Key Navigation Technologies for Facility Agricultural Robots HE Yong, HUANG Zhenyu, YANG Ningyuan, LI Xiyao, WANG Yuwei, FENG Xuping Smart Agriculture    2024, 6 (5): 1-19.   DOI: 10.12133/j.smartag.SA202404006 Abstract （1193）   HTML （294）    PDF（pc） （2130KB）（3995）       Knowledge map    Save [Significance] With the rapid development of robotics technology and the persistently rise of labor costs, the application of robots in facility agriculture is becoming increasingly widespread. These robots can enhance operational efficiency, reduce labor costs, and minimize human errors. However, the complexity and diversity of facility environments, including varying crop layouts and lighting conditions, impose higher demands on robot navigation. Therefore, achieving stable, accurate, and rapid navigation for robots has become a key issue. Advanced sensor technologies and algorithms have been proposed to enhance robots' adaptability and decision-making capabilities in dynamic environments. This not only elevates the automation level of agricultural production but also contributes to more intelligent agricultural management. [Progress] This paper reviews the key technologies of automatic navigation for facility agricultural robots. It details beacon localization, inertial positioning, simultaneous localization and mapping (SLAM) techniques, and sensor fusion methods used in autonomous localization and mapping. Depending on the type of sensors employed, SLAM technology could be subdivided into vision-based, laser-based and fusion systems. Fusion localization is further categorized into data-level, feature-level, and decision-level based on the types and stages of the fused information. The application of SLAM technology and fusion localization in facility agriculture has been increasingly common. Global path planning plays a crucial role in enhancing the operational efficiency and safety of facility aricultural robots. This paper discusses global path planning, classifying it into point-to-point local path planning and global traversal path planning. Furthermore, based on the number of optimization objectives, it was divided into single-objective path planning and multi-objective path planning. In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios. [Conclusions and Prospects] Currently, the challenges for facility agricultural robot navigation include complex scenarios with significant occlusions, cost constraints, low operational efficiency and the lack of standardized platforms and public datasets. These issues not only affect the practical application effectiveness of robots but also constrain the further advancement of the industry. To address these challenges, future research can focus on developing multi-sensor fusion technologies, applying and optimizing advanced algorithms, investigating and implementing multi-robot collaborative operations and establishing standardized and shared data platforms. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming Smart Agriculture    2024, 6 (5): 20-39.   DOI: 10.12133/j.smartag.SA202405022 Abstract （818）   HTML （147）    PDF（pc） （4030KB）（3930）       Knowledge map    Save [Significance] Fruit-picking robot stands as a crucial solution for achieving intelligent fruit harvesting. Significant progress has been made in developing foundational methods for picking robots, such as fruit recognition, orchard navigation, path planning for picking, and robotic arm control, the practical implementation of a seamless picking system that integrates sensing, movement, and picking capabilities still encounters substantial technical hurdles. In contrast to current picking systems, the next generation of fruit-picking robots aims to replicate the autonomous skills exhibited by human fruit pickers. This involves effectively performing ongoing tasks of perception, movement, and picking without human intervention. To tackle this challenge, this review delves into the latest research methodologies and real-world applications in this field, critically assesses the strengths and limitations of existing methods and categorizes the essential components of continuous operation into three sub-modules: local target recognition, global mapping, and operation planning. [Progress] Initially, the review explores methods for recognizing nearby fruit and obstacle targets. These methods encompass four main approaches: low-level feature fusion, high-level feature learning, RGB-D information fusion, and multi-view information fusion, respectively. Each of these approaches incorporates advanced algorithms and sensor technologies for cluttered orchard environments. For example, low-level feature fusion utilizes basic attributes such as color, shapes and texture to distinguish fruits from backgrounds, while high-level feature learning employs more complex models like convolutional neural networks to interpret the contextual relationships within the data. RGB-D information fusion brings depth perception into the mix, allowing robots to gauge the distance to each fruit accurately. Multi-view information fusion tackles the issue of occlusions by combining data from multiple cameras and sensors around the robot, providing a more comprehensive view of the environment and enabling more reliable sensing. Subsequently, the review shifts focus to orchard mapping and scene comprehension on a broader scale. It points out that current mapping methods, while effective, still struggle with dynamic changes in the orchard, such as variations of fruits and light conditions. Improved adaptation techniques, possibly through machine learning models that can learn and adjust to different environmental conditions, are suggested as a way forward. Building upon the foundation of local and global perception, the review investigates strategies for planning and controlling autonomous behaviors. This includes not only the latest advancements in devising movement paths for robot mobility but also adaptive strategies that allow robots to react to unexpected obstacles or changes within the whole environment. Enhanced strategies for effective fruit picking using the Eye-in-Hand system involve the development of more dexterous robotic hands and improved algorithms for precisely predicting the optimal picking point of each fruit. The review also identifies a crucial need for further advancements in the dynamic behavior and autonomy of these technologies, emphasizing the importance of continuous learning and adaptive control systems to improve operational efficiency in diverse orchard environments. [Conclusions and Prospects] The review underscores the critical importance of coordinating perception, movement, and picking modules to facilitate the transition from a basic functional prototype to a practical machine. Moreover, it emphasizes the necessity of enhancing the robustness and stability of core algorithms governing perception, planning, and control, while ensuring their seamless coordination which is a central challenge that emerges. Additionally, the review raises unresolved questions regarding the application of picking robots and outlines future trends, include deeper integration of stereo vision and deep learning, enhanced global vision sampling, and the establishment of standardized evaluation criteria for overall operational performance. The paper can provide references for the eventual development of robust, autonomous, and commercially viable picking robots in the future. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Advances and Development Trend of Mountainous Tractor Leveling and Anti-Rollover System MU Xiaodong, YANG Fuzeng, DUAN Luojia, LIU Zhijie, SONG Zhuoying, LI Zonglin, GUAN Shouqing Smart Agriculture    2024, 6 (3): 1-16.   DOI: 10.12133/j.smartag.SA202312015 Abstract （795）   HTML （100）    PDF（pc） （2448KB）（3655）       Knowledge map    Save [Significance] The mechanization, automation and intelligentization of agricultural equipment are key factors to improve operation efficiency, free up labor force and promote the sustainable development of agriculture. It is also the hot spot of research and development of agricultural machinery industry in the future. In China, hills and mountains serves as vital production bases for agricultural products, accounting for about 70% of the country's land area. In addition, these regions face various environmental factors such as steep slopes, narrow road, small plots, complex terrain and landforms, as well as harsh working environment. Moreover, there is a lack of reliable agricultural machinery support across various production stages, along with a shortage of theoretical frameworks to guide the research and development of agricultural machinery tailored to hilly and mountainous locales. [Progress] This article focuses on the research advances of tractor leveling and anti-overturning systems in hilly and mountainous areas, including tractor body, cab and seat leveling technology, tractor rear suspension and implement leveling slope adaptive technology, and research progress on tractor anti-overturning protection devices and warning technology. The vehicle body leveling mechanism can be roughly divided into five types based on its different working modes: parallel four bar, center of gravity adjustable, hydraulic differential high, folding and twisting waist, and omnidirectional leveling. These mechanisms aim to address the issue of vehicle tilting and easy overturning when traversing or working on sloping or rugged roads. By keeping the vehicle body posture horizontal or adjusting the center of gravity within a stable range, the overall driving safety of the vehicle can be improved to ensure the accuracy of operation. Leveling the driver's cab and seats can mitigate the lateral bumps experienced by the driver during rough or sloping operations, reducing driver fatigue and minimizing strain on the lumbar and cervical spine, thereby enhancing driving comfort. The adaptive technology of tractor rear suspension and implement leveling on slopes can ensure that the tractor maintains consistent horizontal contact with the ground in hilly and mountainous areas, avoiding changes in the posture of the suspended implement with the swing of the body or the driving path, which may affect the operation effect. The tractor rollover protection device and warning technology have garnered significant attention in recent years. Prioritizing driver safety, rollover warning system can alert the driver in advance of the dangerous state of the tractor, automatically adjust the vehicle before rollover, or automatically open the rollover protection device when it is about to rollover, and timely send accident reports to emergency contacts, thereby ensuring the safety of the driver to the greatest extent possible. [Conclusions and Prospects] The future development directions of hill and mountain tractor leveling, anti-overturning early warning, unmanned, automatic technology were looked forward: Structure optimization, high sensitivity, good stability of mountain tractor leveling system research; Study on copying system of agricultural machinery with good slope adaptability; Research on anti-rollover early warning technology of environment perception and automatic interference; Research on precision navigation technology, intelligent monitoring technology and remote scheduling and management technology of agricultural machinery; Theoretical study on longitudinal stability of sloping land. This review could provide reference for the research and development of high reliability and high safety mountain tractor in line with the complex working environment in hill and mountain areas. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Advances and Prospects on Rapid Acquisition Technology of Farmland Soil Physical and Chemical Parameters QI Jiangtao, CHENG Panting, GAO Fangfang, GUO Li, ZHANG Ruirui Smart Agriculture    2024, 6 (3): 17-33.   DOI: 10.12133/j.smartag.SA202404003 Abstract （748）   HTML （114）    PDF（pc） （1524KB）（5188）       Knowledge map    Save [Significance] Soil stands as the fundamental pillar of agricultural production, with its quality being intrinsically linked to the efficiency and sustainability of farming practices. Historically, the intensive cultivation and soil erosion have led to a marked deterioration in some arable lands, characterized by a sharp decrease in soil organic matter, diminished fertility, and a decline in soil's structural integrity and ecological functions. In the strategic framework of safeguarding national food security and advancing the frontiers of smart and precision agriculture, the march towards agricultural modernization continues apace, intensifying the imperative for meticulous soil quality management. Consequently, there is an urgent need for the rrapid acquisition of soil's physical and chemical parameters. Interdisciplinary scholars have delved into soil monitoring research, achieving notable advancements that promise to revolutionize the way we understand and manage soil resource. [Progress] Utilizing the the Web of Science platform, a comprehensive literature search was conducted on the topic of "soil," further refined with supplementary keywords such as "electrochemistry", "spectroscopy", "electromagnetic", "ground-penetrating radar", and "satellite". The resulting literature was screened, synthesized, and imported into the CiteSpace visualization tool. A keyword emergence map was yielded, which delineates the trajectory of research in soil physical and chemical parameter detection technology. Analysis of the keyword emergence map reveals a paradigm shift in the acquisition of soil physical and chemical parameters, transitioning from conventional indoor chemical and spectrometry analyses to outdoor, real-time detection methods. Notably, soil sensors integrated into drones and satellites have garnered considerable interest. Additionally, emerging monitoring technologies, including biosensing and terahertz spectroscopy, have made their mark in recent years. Drawing from this analysis, the prevailing technologies for soil physical and chemical parameter information acquisition in agricultural fields have been categorized and summarized. These include: 1. Rapid Laboratory Testing Techniques: Primarily hinged on electrochemical and spectrometry analysis, these methods offer the dual benefits of time and resource efficiency alongside high precision; 2. Rapid Near-Ground Sensing Techniques: Leveraging electromagnetic induction, ground-penetrating radar, and various spectral sensors (multispectral, hyperspectral, and thermal infrared), these techniques are characterized by their high detection accuracy and swift operation. 3. Satellite Remote Sensing Techniques: Employing direct inversion, indirect inversion, and combined analysis methods, these approaches are prized for their efficiency and extensive coverage. 4. Innovative Rapid Acquisition Technologies: Stemming from interdisciplinary research, these include biosensing, environmental magnetism, terahertz spectroscopy, and gamma spectroscopy, each offering novel avenues for soil parameter detection. An in-depth examination and synthesis of the principles, applications, merits, and limitations of each technology have been provided. Moreover, a forward-looking perspective on the future trajectory of soil physical and chemical parameter acquisition technology has been offered, taking into account current research trends and hotspots. [Conclusions and Prospects] Current advancements in the technology for rapaid acquiring soil physical and chemical parameters in agricultural fields have been commendable, yet certain challenges persist. The development of near-ground monitoring sensors is constrained by cost, and their reliability, adaptability, and specialization require enhancement to effectively contend with the intricate and varied conditions of farmland environments. Additionally, remote sensing inversion techniques are confronted with existing limitations in data acquisition, processing, and application. To further develop the soil physical and chemical parameter acquisition technology and foster the evolution of smart agriculture, future research could beneficially delve into the following four areas: Designing portable, intelligent, and cost-effective near-ground soil information acquisition systems and equipment to facilitate rapid on-site soil information detection; Enhancing the performance of low-altitude soil information acquisition platforms and refine the methods for data interpretation to ensure more accurate insights; Integrating multifactorial considerations to construct robust satellite remote sensing inversion models, leveraging diverse and open cloud computing platforms for in-depth data analysis and mining; Engaging in thorough research on the fusion of multi-source data in the acquisition of soil physical and chemical parameter information, developing soil information sensing algorithms and models with strong generalizability and high reliability to achieve rapaid, precise, and intelligent acquisition of soil parameters. 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Big Models in Agriculture: Key Technologies, Application and Future Directions GUO Wang, YANG Yusen, WU Huarui, ZHU Huaji, MIAO Yisheng, GU Jingqiu Smart Agriculture    2024, 6 (2): 1-13.   DOI: 10.12133/j.smartag.SA202403015 Abstract （2802）   HTML （497）    PDF（pc） （1482KB）（3677）       Knowledge map    Save [Significance] Big Models, or Foundation Models, have offered a new paradigm in smart agriculture. These models, built on the Transformer architecture, incorporate numerous parameters and have undergone extensive training, often showing excellent performance and adaptability, making them effective in addressing agricultural issues where data is limited. Integrating big models in agriculture promises to pave the way for a more comprehensive form of agricultural intelligence, capable of processing diverse inputs, making informed decisions, and potentially overseeing entire farming systems autonomously. [Progress] The fundamental concepts and core technologies of big models are initially elaborated from five aspects: the generation and core principles of the Transformer architecture, scaling laws of extending big models, large-scale self-supervised learning, the general capabilities and adaptions of big models, and the emerging capabilities of big models. Subsequently, the possible application scenarios of the big model in the agricultural field are analyzed in detail, the development status of big models is described based on three types of the models: Large language models (LLMs), large vision models (LVMs), and large multi-modal models (LMMs). The progress of applying big models in agriculture is discussed, and the achievements are presented. [Conclusions and Prospects] The challenges and key tasks of applying big models technology in agriculture are analyzed. Firstly, the current datasets used for agricultural big models are somewhat limited, and the process of constructing these datasets can be both expensive and potentially problematic in terms of copyright issues. There is a call for creating more extensive, more openly accessible datasets to facilitate future advancements. Secondly, the complexity of big models, due to their extensive parameter counts, poses significant challenges in terms of training and deployment. However, there is optimism that future methodological improvements will streamline these processes by optimizing memory and computational efficiency, thereby enhancing the performance of big models in agriculture. Thirdly, these advanced models demonstrate strong proficiency in analyzing image and text data, suggesting potential future applications in integrating real-time data from IoT devices and the Internet to make informed decisions, manage multi-modal data, and potentially operate machinery within autonomous agricultural systems. Finally, the dissemination and implementation of these big models in the public agricultural sphere are deemed crucial. The public availability of these models is expected to refine their capabilities through user feedback and alleviate the workload on humans by providing sophisticated and accurate agricultural advice, which could revolutionize agricultural practices. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Intelligent Identification of Crop Agronomic Traits and Morphological Structure Phenotypes: A Review ZHANG Jianhua, YAO Qiong, ZHOU Guomin, WU Wendi, XIU Xiaojie, WANG Jian Smart Agriculture    2024, 6 (2): 14-27.   DOI: 10.12133/j.smartag.SA202401015 Abstract （1660）   HTML （110）    PDF（pc） （1376KB）（5916）       Knowledge map    Save [Significance] The crop phenotype is the visible result of the complex interplay between crop genes and the environment. It reflects the physiological, ecological, and dynamic aspects of crop growth and development, serving as a critical component in the realm of advanced breeding techniques. By systematically analyzing crop phenotypes, researchers can gain valuable insights into gene function and identify genetic factors that influence important crop traits. This information can then be leveraged to effectively harness germplasm resources and develop breakthrough varieties. Utilizing data-driven, intelligent, dynamic, and non-invasive methods for measuring crop phenotypes allows researchers to accurately capture key growth traits and parameters, providing essential data for breeding and selecting superior crop varieties throughout the entire growth cycle. This article provides an overview of intelligent identification technologies for crop agronomic traits and morphological structural phenotypes. [Progress] Crop phenotype acquisition equipment serves as the essential foundation for acquiring, analyzing, measuring, and identifying crop phenotypes. This equipment enables detailed monitoring of crop growth status. The article presents an overview of the functions, performance, and applications of the leading high-throughput crop phenotyping platforms, as well as an analysis of the characteristics of various sensing and imaging devices used to obtain crop phenotypic information. The rapid advancement of high-throughput crop phenotyping platforms and sensory imaging equipment has facilitated the integration of cutting-edge imaging technology, spectroscopy technology, and deep learning algorithms. These technologies enable the automatic and high-throughput acquisition of yield, resistance, quality, and other relevant traits of large-scale crops, leading to the generation of extensive multi-dimensional, multi-scale, and multi-modal crop phenotypic data. This advancement supports the rapid progression of crop phenomics. The article also discusses the research progress of intelligent recognition technologies for agronomic traits such as crop plant height acquisition, crop organ detection, and counting, as well as crop ideotype recognition, crop morphological information measurement, and crop three-dimensional reconstruction for morphological structure intelligent recognition. Furthermore, this article outlines the main challenges faced in this field, including: difficulties in data collection in complex environments, high requirements for data scale, diversity, and preprocessing, the need to improve the lightweight nature and generalization ability of models, as well as the high cost of data collection equipment and the need to enhance practicality. [Conclusions and Prospects] Finally, this article puts forward the development directions of crop phenotype intelligent recognition technology, including: developing new and low cost intelligent field equipment for acquiring and analyzing crop phenotypes, enhancing the standardization and consistency of field crop phenotype acquisition, strengthening the generality of intelligent crop phenotype recognition models, researching crop phenotype recognition methods that involve multi-perspective, multimodal, multi-point continuous analysis, and spatiotemporal feature fusion, as well as improving model interpretability. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Sensor: Research Progress, Challenges and Perspectives WANG Rujing Smart Agriculture    2024, 6 (1): 1-17.   DOI: 10.12133/j.smartag.SA202401017 Abstract （1830）   HTML （413）    PDF（pc） （1179KB）（10653）       Knowledge map    Save Significance Agricultural sensor is the key technology for developing modern agriculture. Agricultural sensor is a kind of detection device that can sense and convert physical signal, which is related to the agricultural environment, plants and animals, into an electrical signal. Agricultural sensors could be applied to monitor crops and livestock in different agricultural environments, including weather, water, atmosphere and soil. It is also an important driving force to promote the iterative upgrading of agricultural technology and change agricultural production methods. Progress The different agricultural sensors are categorized, the cutting-edge research trends of agricultural sensors are analyzed, and summarizes the current research status of agricultural sensors are summarized in different application scenarios. Moreover, a deep analysis and discussion of four major categories is conducted, which include agricultural environment sensors, animal and plant life information sensors, agricultural product quality and safety sensors, and agricultural machinery sensors. The process of research, development, the universality and limitations of the application of the four types of agricultural sensors are summarized. Agricultural environment sensors are mainly used for real-time monitoring of key parameters in agricultural production environments, such as the quality of water, gas, and soil. The soil sensors provide data support for precision irrigation, rational fertilization, and soil management by monitoring indicators such as soil humidity, pH, temperature, nutrients, microorganisms, pests and diseases, heavy metals and agricultural pollution, etc. Monitoring of dissolved oxygen, pH, nitrate content, and organophosphorus pesticides in irrigation and aquaculture water through water sensors ensures the rational use of water resources and water quality safety. The gas sensor monitors the atmospheric CO2, NH3, C2H2, CH4 concentration, and other information, which provides the appropriate environmental conditions for the growth of crops in greenhouses. The animal life information sensor can obtain the animal's growth, movement, physiological and biochemical status, which include movement trajectory, food intake, heart rate, body temperature, blood pressure, blood glucose, etc. The plant life information sensors monitor the plant's health and growth, such as volatile organic compounds of the leaves, surface temperature and humidity, phytohormones, and other parameters. Especially, the flexible wearable plant sensors provide a new way to measure plant physiological characteristics accurately and monitor the water status and physiological activities of plants non-destructively and continuously. These sensors are mainly used to detect various indicators in agricultural products, such as temperature and humidity, freshness, nutrients, and potentially hazardous substances (e.g., bacteria, pesticide residues, heavy metals, etc. Agricultural machinery sensors can achieve real-time monitoring and controlling of agricultural machinery to achieve real-time cultivation, planting, management, and harvesting, automated operation of agricultural machinery, and accurate application of pesticide, fertilizer. [Conclusions and Prospects In the challenges and prospects of agricultural sensors, the core bottlenecks of large-scale application of agricultural sensors at the present stage are analyzed in detail. These include low-cost, specialization, high stability, and adaptive intelligence of agricultural sensors. Furthermore, the concept of "ubiquitous sensing in agriculture" is proposed, which provides ideas and references for the research and development of agricultural sensor technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Robots: Technology Progress, Challenges and Trends ZHAO Chunjiang, FAN Beibei, LI Jin, FENG Qingchun Smart Agriculture    2023, 5 (4): 1-15.   DOI: 10.12133/j.smartag.SA202312030 Abstract （2809）   HTML （426）    PDF（pc） （2498KB）（4222）       Knowledge map    Save [Significance] Autonomous and intelligent agricultural machinery, characterized by green intelligence, energy efficiency, and reduced emissions, as well as high intelligence and man-machine collaboration, will serve as the driving force behind global agricultural technology advancements and the transformation of production methods in the context of smart agriculture development. Agricultural robots, which utilize intelligent control and information technology, have the unique advantage of replacing manual labor. They occupy the strategic commanding heights and competitive focus of global agricultural equipment and are also one of the key development directions for accelerating the construction of China's agricultural power. World agricultural powers and China have incorporated the research, development, manufacturing, and promotion of agricultural robots into their national strategies, respectively strengthening the agricultural robot policy and planning layout based on their own agricultural development characteristics, thus driving the agricultural robot industry into a stable growth period. [Progress] This paper firstly delves into the concept and defining features of agricultural robots, alongside an exploration of the global agricultural robot development policy and strategic planning blueprint. Furthermore, sheds light on the growth and development of the global agricultural robotics industry; Then proceeds to analyze the industrial backdrop, cutting-edge advancements, developmental challenges, and crucial technology aspects of three representative agricultural robots, including farmland robots, orchard picking robots, and indoor vegetable production robots. Finally, summarizes the disparity between Chinese agricultural robots and their foreign counterparts in terms of advanced technologies. (1) An agricultural robot is a multi-degree-of-freedom autonomous operating equipment that possesses accurate perception, autonomous decision-making, intelligent control, and automatic execution capabilities specifically designed for agricultural environments. When combined with artificial intelligence, big data, cloud computing, and the Internet of Things, agricultural robots form an agricultural robot application system. This system has relatively mature applications in key processes such as field planting, fertilization, pest control, yield estimation, inspection, harvesting, grafting, pruning, inspection, harvesting, transportation, and livestock and poultry breeding feeding, inspection, disinfection, and milking. Globally, agricultural robots, represented by plant protection robots, have entered the industrial application phase and are gradually realizing commercialization with vast market potential. (2) Compared to traditional agricultural machinery and equipment, agricultural robots possess advantages in performing hazardous tasks, executing batch repetitive work, managing complex field operations, and livestock breeding. In contrast to industrial robots, agricultural robots face technical challenges in three aspects. Firstly, the complexity and unstructured nature of the operating environment. Secondly, the flexibility, mobility, and commoditization of the operation object. Thirdly, the high level of technology and investment required. (3) Given the increasing demand for unmanned and less manned operations in farmland production, China's agricultural robot research, development, and application have started late and progressed slowly. The existing agricultural operation equipment still has a significant gap from achieving precision operation, digital perception, intelligent management, and intelligent decision-making. The comprehensive performance of domestic products lags behind foreign advanced counterparts, indicating that there is still a long way to go for industrial development and application. Firstly, the current agricultural robots predominantly utilize single actuators and operate as single machines, with the development of multi-arm cooperative robots just emerging. Most of these robots primarily engage in rigid operations, exhibiting limited flexibility, adaptability, and functionality. Secondly, the perception of multi-source environments in agricultural settings, as well as the autonomous operation of agricultural robot equipment, relies heavily on human input. Thirdly, the progress of new teaching methods and technologies for human-computer natural interaction is rather slow. Lastly, the development of operational infrastructure is insufficient, resulting in a relatively low degree of "mechanization". [Conclusions and Prospects] The paper anticipates the opportunities that arise from the rapid growth of the agricultural robotics industry in response to the escalating global shortage of agricultural labor. It outlines the emerging trends in agricultural robot technology, including autonomous navigation, self-learning, real-time monitoring, and operation control. In the future, the path planning and navigation information perception of agricultural robot autonomy are expected to become more refined. Furthermore, improvements in autonomous learning and cross-scenario operation performance will be achieved. The development of real-time operation monitoring of agricultural robots through digital twinning will also progress. Additionally, cloud-based management and control of agricultural robots for comprehensive operations will experience significant growth. Steady advancements will be made in the innovation and integration of agricultural machinery and techniques. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing MA Yujing, WU Shangrong, YANG Peng, CAO Hong, TAN Jieyang, ZHAO Rongkun Smart Agriculture    2023, 5 (3): 1-16.   DOI: 10.12133/j.smartag.SA202303002 Abstract （874）   HTML （179）    PDF（pc） （837KB）（5411）       Knowledge map    Save [Significance] Oil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology. [Progress] This paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was expounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future. [Conclusions and Prospects] The paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estimation and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Diagnosis of Grapevine Leafroll Disease Severity Infection via UAV Remote Sensing and Deep Learning LIU Yixue, SONG Yuyang, CUI Ping, FANG Yulin, SU Baofeng Smart Agriculture    2023, 5 (3): 49-61.   DOI: 10.12133/j.smartag.SA202308013 Abstract （779）   HTML （119）    PDF（pc） （3044KB）（1024）       Knowledge map    Save [Objective] Wine grapes are severely affected by leafroll disease, which affects their growth, and reduces the quality of the color, taste, and flavor of wine. Timely and accurate diagnosis of leafroll disease severity is crucial for preventing and controlling the disease, improving the wine grape fruit quality and wine-making potential. Unmanned aerial vehicle (UAV) remote sensing technology provides high-resolution images of wine grape vineyards, which can capture the features of grapevine canopies with different levels of leafroll disease severity. Deep learning networks extract complex and high-level features from UAV remote sensing images and perform fine-grained classification of leafroll disease infection severity. However, the diagnosis of leafroll disease severity is challenging due to the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. [Method] A novel method for diagnosing leafroll disease severity was developed at a canopy scale using UAV remote sensing technology and deep learning. The main challenge of this task was the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. To address this challenge, a method that combined deep learning fine-grained classification and generative adversarial networks (GANs) was proposed. In the first stage, the GANformer, a Transformer-based GAN model was used, to generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity. To further analyze the image generation effect of GANformer. The t-distributed stochastic neighbor embedding (t-SNE) to visualize the learned features of real and simulated images. In the second stage, the CA-Swin Transformer, an improved image classification model based on the Swin Transformer and channel attention mechanism was used, to classify the patch images into different classes of leafroll disease infection severity. CA-Swin Transformer could also use a self-attention mechanism to capture the long-range dependencies of image patches and enhance the feature representation of the Swin Transformer model by adding a channel attention mechanism after each Transformer layer. The channel attention (CA) mechanism consisted of two fully connected layers and an activation function, which could extract correlations between different channels and amplify the informative features. The ArcFace loss function and instance normalization layer was also used to enhance the fine-grained feature extraction and downsampling ability for grapevine canopy images. The UAV images of wine grape vineyards were collected and processed into orthomosaic images. They labeled into three categories: healthy, moderate infection, and severe infection using the in-field survey data. A sliding window method was used to extract patch images and labels from orthomosaic images for training and testing. The performance of the improved method was compared with the baseline model using different loss functions and normalization methods. The distribution of leafroll disease severity was mapped in vineyards using the trained CA-Swin Transformer model. [Results and Discussions] The experimental results showed that the GANformer could generate high-quality virtual canopy images of grapevines with an FID score of 93.20. The images generated by GANformer were visually very similar to real images and could produce images with different levels of leafroll disease severity. The T-SNE visualization showed that the features of real and simulated images were well clustered and separated in two-dimensional space, indicating that GANformer learned meaningful and diverse features, which enriched the image dataset. Compared to CNN-based deep learning models, Transformer-based deep learning models had more advantages in diagnosing leafroll disease infection. Swin Transformer achieved an optimal accuracy of 83.97% on the enhanced dataset, which was higher than other models such as GoogLeNet, MobileNetV2, NasNet Mobile, ResNet18, ResNet50, CVT, and T2TViT. It was found that replacing the cross entropy loss function with the ArcFace loss function improved the classification accuracy by 1.50%, and applying instance normalization instead of layer normalization further improved the accuracy by 0.30%. Moreover, the proposed channel attention mechanism, named CA-Swin Transformer, enhanced the feature representation of the Swin Transformer model, achieved the highest classification accuracy on the test set, reaching 86.65%, which was 6.54% higher than using the Swin Transformer on the original test dataset. By creating a distribution map of leafroll disease severity in vineyards, it was found that there was a certain correlation between leafroll disease severity and grape rows. Areas with a larger number of severe leafroll diseases caused by Cabernet Sauvignon were more prone to have missing or weak plants. [Conclusions] A novel method for diagnosing grapevine leafroll disease severity at a canopy scale using UAV remote sensing technology and deep learning was proposed. This method can generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity using GANformer, and classify them into different classes using CA-Swin Transformer. This method can also map the distribution of leafroll disease severity in vineyards using a sliding window method, and provides a new approach for crop disease monitoring based on UAV remote sensing technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Multi-Focal Green Plant Image Fusion Method Based on Stationary Wavelet Transform and Parameter-Adaptation Dual Channel Pulse-Coupled Neural Network LI Jiahao, QU Hongjun, GAO Mingzhe, TONG Dezhi, GUO Ya Smart Agriculture    2023, 5 (3): 121-131.   DOI: 10.12133/j.smartag.SA202308005 Abstract （355）   HTML （31）    PDF（pc） （1435KB）（692）       Knowledge map    Save [Objective] To construct the 3D point cloud model of green plants a large number of clear images are needed. Due to the limitation of the depth of field of the lens, part of the image would be out of focus when the green plant image with a large depth of field is collected, resulting in problems such as edge blurring and texture detail loss, which greatly affects the accuracy of the 3D point cloud model. However, the existing processing algorithms are difficult to take into account both processing quality and processing speed, and the actual effect is not ideal. The purpose of this research is to improve the quality of the fused image while taking into account the processing speed. [Methods] A plant image fusion method based on non-subsampled shearlet transform (NSST) based parameter-adaptive dual channel pulse-coupled neural network (PADC-PCNN) and stationary wavelet transform (SWT) was proposed. Firstly, the RGB image of the plant was separated into three color channels, and the G channel with many features such as texture details was decomposed by NSST in four decomposition layers and 16 directions, which was divided into one group of low frequency subbands and 64 groups of high frequency subbands. The low frequency subband used the gradient energy fusion rule, and the high frequency subband used the PADC-PCNN fusion rule. In addition, the weighting of the eight-neighborhood modified Laplacian operator was used as the link strength of the high-frequency fusion part, which enhanced the fusion effect of the detailed features. At the same time, for the R and B channels with more contour information and background information, a SWT with fast speed and translation invariance was used to suppress the pseudo-Gibbs effect. Through the high-precision and high-stability multi-focal length plant image acquisition system, 480 images of 8 experimental groups were collected. The 8 groups of data were divided into an indoor light group, natural light group, strong light group, distant view group, close view group, overlooking group, red group, and yellow group. Meanwhile, to study the application range of the algorithm, the focus length of the collected clear plant image was used as the reference (18 mm), and the image acquisition was adjusted four times before and after the step of 1.5 mm, forming the multi-focus experimental group. Subjective evaluation and objective evaluation were carried out for each experimental group to verify the performance of the algorithm. Subjective evaluation was analyzed through human eye observation, detail comparison, and other forms, mainly based on the human visual effect. The image fusion effect of the algorithm was evaluated using four commonly used objective indicators, including average gradient (AG), spatial frequency (SF), entropy (EN), and standard deviation (SD). [Results and Discussions] The proposed PADC-PCNN-SWT algorithm and other five algorithms of common fast guided filtering algorithm (FGF), random walk algorithm (RW), non-subsampled shearlet transform based PCNN (NSST-PCNN) algorithm, SWT algorithm and non-subsampled shearlet transform based parameter-adaptive dual-channel pulse-coupled neural network (NSST-PADC) and were compared. In the objective evaluation data except for the red group and the yellow group, each index of the PADC-PCNN-SWT algorithm was second only to the NSST-PADC algorithm, but the processing speed was 200.0% higher than that of the NSST-PADC algorithm on average. At the same time, compared with the FDF, RW, NSST-PCNN, and SWT algorithms, the PADC-PCN -SWT algorithm improved the clarity index by 5.6%, 8.1%, 6.1%, and 17.6%, respectively, and improved the spatial frequency index by 2.9%, 4.8%, 7.1%, and 15.9%, respectively. However, the difference between the two indicators of information entropy and standard deviation was less than 1%, and the influence was ignored. In the yellow group and the red group, the fusion quality of the non-green part of the algorithm based on PADC-PCNN-SWT was seriously degraded. Compared with other algorithms, the sharpness index of the algorithm based on PADC-PCNN-SWT decreased by an average of 1.1%, and the spatial frequency decreased by an average of 5.1%. However, the indicators of the green part of the fused image were basically consistent with the previous several groups of experiments, and the fusion effect was good. Therefore, the algorithm based on PADC-PCNN-SWT only had a good fusion effect on green plants. Finally, by comparing the quality of four groups of fused images with different focal length ranges, the results showed that the algorithm based on PADC-PCNN-SWT had a better contour and color restoration effect for out-of-focus images in the range of 15-21 mm, and the focusing range based on PADC-PCNN-SWT was about 6 mm. [Conclusions] The multi-focal length image fusion algorithm based on PADC-PCNN-SWT achieved better detail fusion performance and higher image fusion efficiency while ensuring fusion quality, providing high-quality data, and saving a lot of time for building 3D point cloud model of green plants. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Agricultural Knowledge Intelligent Service Technology: A Review ZHAO Chunjiang Smart Agriculture    2023, 5 (2): 126-148.   DOI: 10.12133/j.smartag.SA202306002 Abstract （2345）   HTML （457）    PDF（pc） （3579KB）（26714）       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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Digital Twin for Agricultural Machinery: From Concept to Application GUO Dafang, DU Yuefeng, WU Xiuheng, HOU Siyu, LI Xiaoyu, ZHANG Yan'an, CHEN Du Smart Agriculture    2023, 5 (2): 149-160.   DOI: 10.12133/j.smartag.SA202305007 Abstract （1091）   HTML （173）    PDF（pc） （2531KB）（1974）       Knowledge map    Save Significance Agricultural machinery serves as the fundamental support for implementing advanced agricultural production concepts. The key challenge for the future development of smart agriculture lies in how to enhance the design, manufacturing, operation, and maintenance of these machines to fully leverage their capabilities. To address this, the concept of the digital twin has emerged as an innovative approach that integrates various information technologies and facilitates the integration of virtual and real-world interactions. By providing a deeper understanding of agricultural machinery and its operational processes, the digital twin offers solutions to the complexity encountered throughout the entire lifecycle, from design to recycling. Consequently, it contributes to an all-encompassing enhancement of the quality of agricultural machinery operations, enabling them to better meet the demands of agricultural production. Nevertheless, despite its significant potential, the adoption of the digital twin for agricultural machinery is still at an early stage, lacking the necessary theoretical guidance and methodological frameworks to inform its practical implementation. Progress Drawing upon the successful experiences of the author's team in the digital twin for agricultural machinery, this paper presents an overview of the research progress made in digital twin. It covers three main areas: The digital twin in a general sense, the digital twin in agriculture, and the digital twin for agricultural machinery. The digital twin is conceptualized as an abstract notion that combines model-based system engineering and cyber-physical systems, facilitating the integration of virtual and real-world environments. This paper elucidates the relevant concepts and implications of digital twin in the context of agricultural machinery. It points out that the digital twin for agricultural machinery aims to leverage advanced information technology to create virtual models that accurately describe agricultural machinery and its operational processes. These virtual models act as a carrier, driven by data, to facilitate interaction and integration between physical agricultural machinery and their digital counterparts, consequently yielding enhanced value. Additionally, it proposes a comprehensive framework comprising five key components: Physical entities, virtual models, data and connectivity, system services, and business applications. Each component's functions operational mechanism, and organizational structure are elucidated. The development of the digital twin for agricultural machinery is still in its conceptual phase, and it will require substantial time and effort to gradually enhance its capabilities. In order to advance further research and application of the digital twin in this domain, this paper integrates relevant theories and practical experiences to propose an implementation plan for the digital twin for agricultural machinery. The macroscopic development process encompasses three stages: Theoretical exploration, practical application, and summarization. The specific implementation process entails four key steps: Intelligent upgrading of agricultural machinery, establishment of information exchange channels, construction of virtual models, and development of digital twin business applications. The implementation of digital twin for agricultural machinery comprises four stages: Pre-research, planning, implementation, and evaluation. The digital twin serves as a crucial link and bridge between agricultural machinery and the smart agriculture. It not only facilitates the design and manufacturing of agricultural machinery, aligning them with the realities of agricultural production and supporting the advancement of advanced manufacturing capabilities, but also enhances the operation, maintenance, and management of agricultural production to better meet practical requirements. This, in turn, expedites the practical implementation of smart agriculture. To fully showcase the value of the digital twin for agricultural machinery, this paper addresses the existing challenges in the design, manufacturing, operation, and management of agricultural machinery. It expounds the methods by which the digital twin can address these challenges and provides a technical roadmap for empowering the design, manufacturing, operation, and management of agricultural machinery through the use of the digital twin. In tackling the critical issue of leveraging the digital twin to enhance the operational quality of agricultural machinery, this paper presents two research cases focusing on high-powered tractors and large combine harvesters. These cases validate the feasibility of the digital twin in improving the quality of plowing operations for high-powered tractors and the quality of grain harvesting for large combine harvesters. Conclusions and Prospects This paper serves as a reference for the development of research on digital twin for agricultural machinery, laying a theoretical foundation for empowering smart agriculture and intelligent equipment with the digital twin. The digital twin provides a new approach for the transformation and upgrade of agricultural machinery, offering a new path for enhancing the level of agricultural mechanization and presenting new ideas for realizing smart agriculture. However, existing digital twin for agricultural machinery is still in its early stages, and there are a series of issues that need to be explored. It is necessary to involve more professionals from relevant fields to advance the research in this area. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （3427）   HTML （785）    PDF（pc） （2582KB）（7284）       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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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 （1989）   HTML （276）    PDF（pc） （853KB）（4200）       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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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 （2043）   HTML （499）    PDF（pc） （2824KB）（3338）       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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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 （1453）   HTML （232）    PDF（pc） （1097KB）（2826）       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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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 （3595）   HTML （859）    PDF（pc） （937KB）（10352）       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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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 （1911）   HTML （205）    PDF（pc） （959KB）（4685）       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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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 （1061）   HTML （102）    PDF（pc） （2594KB）（6715）       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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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 （3393）   HTML （503）    PDF（pc） （1983KB）（2823）       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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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 （1526）   HTML （146）    PDF（pc） （2085KB）（2612）       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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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 （4761）   HTML （7784）    PDF（pc） （2650KB）（5510）       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 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 （4550）   HTML （4270）    PDF（pc） （2346KB）（14131）       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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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 （1649）   HTML （1729）    PDF（pc） （2504KB）（2063）       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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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 （7649）   HTML （7389）    PDF（pc） （2843KB）（13878）       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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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 （2082）   HTML （2701）    PDF（pc） （1967KB）（1871）       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 （2058）   HTML （861）    PDF（pc） （1962KB）（3448）       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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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 （1583）   HTML （1435）    PDF（pc） （1634KB）（4033）       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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Characteristics Analysis and Challenges for Fault Diagnosis in Solar Insecticidal Lamps Internet of Things YANG Xing, SHU Lei, HUANG Kai, LI Kailiang, HUO Zhiqiang, WANG Yanfei, WANG Xinyi, LU Qiaoling, ZHANG Yacheng Smart Agriculture    2020, 2 (2): 11-27.   DOI: 10.12133/j.smartag.2020.2.2.202005-SA002 Abstract （2051）   HTML （2503）    PDF（pc） （3592KB）（1877）       Knowledge map    Save Solar insecticidal lamps Internet of Things (SIL-IoTs) is a novel physical agricultural pest control implement, which is an emerging paradigm that extends Internet of Things technology towards Solar Insecticidal Lamp (SIL). SIL-IoTs is composed of SIL nodes with functions of preventing and controlling of agricultural migratory pests with phototaxis feature, which can be deployed over a vast region for the purpose of ensuring pests outbreak area location, reducing pesticide dosage and monitoring agricultural environmental conditions. SIL-IoTs is widely used in agricultural production, and a number of studies have been conducted. However, in most current research projects, fault diagnosis has not been taken into consideration, despite the fact that SIL-IoTs faults have an adverse influence on the development and application of SIL-IoTs. Based on this background, this research aims to analyze the characteristics and challenges of fault diagnosis in SIL-IoTs, which naturally leads to a great number of open research issues outlined afterward. Firstly, an overview and state-of-art of SIL-IoTs were introduced, and the importance of fault diagnosis in SIL-IoTs was analyzed. Secondly, faults of SIL nodes were listed and classified into different types of Wireless Sensor Networks (WSNs) faults. Furthermore, WSNs faults were classified into behavior-based, time-based, component-based, and area affected-based faults. Different types of fault diagnosis algorithms (i.e., statistic method, probability method, hierarchical routing method, machine learning method, topology control method, and mobile sink method) in WSNs were discussed and summarized. Moreover, WSNs fault diagnosis strategies were classified into behavior-based strategies (i.e., active type and positive type), monitoring-based strategies (i.e., continuous type, periodic type, direct type, and indirect type) and facility-based strategies (i.e., centralized type, distributed type and hybrid type). Based on above algorithms and strategies, four kinds of fault phenomena: 1) abnormal background data, 2) abnormal communication of some nodes, 3) abnormal communication of the whole SIL-IoTs, and 4) normal performance with abnormal behavior actually were introduced, and fault diagnosis tools (i.e., Sympathy, Clairvoyant, SNIF and Dustminer) which were adapted to the mentioned fault phenomena were analyzed. Finally, four challenges of fault diagnosis in SIL-IoTs were highlighted, i.e., 1) the complex deployment environment of SIL nodes, leading to the fault diagnosis challenges of heterogeneous WSNs under the condition of unequal energy harvesting, 2) SIL nodes task conflict, resulting from the interference of high voltage discharge, 3) signal loss of continuous area nodes, resulting in the regional link fault, and 4) multiple failure situations of fault diagnosis. To sum up, fault diagnosis plays a vital role in ensuring the reliability, real-time data transmission, and insecticidal efficiency of SIL-IoTs. This work can also be extended for various types of smart agriculture applications and provide fault diagnosis references. 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 （2711）   HTML （7182）    PDF（pc） （1726KB）（4250）       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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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 （3045）   HTML （5507）    PDF（pc） （1588KB）（6159）       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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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 （3326）   HTML （4655）    PDF（pc） （871KB）（6244）       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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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 （4381）   HTML （7208）    PDF（pc） （566KB）（7024）       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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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 （2185）   HTML （2177）    PDF（pc） （1240KB）（25229）       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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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 （3263）   HTML （10049）    PDF（pc） （964KB）（3688）       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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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 （3127）   HTML （2595）    PDF（pc） （2880KB）（5177）       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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