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    Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology | Open Access
    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
    Abstract1460)   HTML281)    PDF (1983KB)(785)      

    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.

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    Study on the Micro-Phenotype of Different Types of Maize Kernels Based on Micro-CT | Open Access
    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
    Abstract682)   HTML63)    PDF (2085KB)(292)      

    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.

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    Advances and Progress of Agricultural Machinery and Sensing Technology Fusion | Open Access
    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
    Abstract2471)   HTML7397)    PDF (2650KB)(1275)      

    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.

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    Research Status and Development Direction of Design and Control Technology of Fruit and Vegetable Picking Robot System | Open Access
    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
    Abstract1685)   HTML3887)    PDF (2346KB)(1321)      

    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.

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    State-of-the-Art and Prospect of Automatic Navigation and Measurement Techniques Application in Conservation Tillage | Open Access
    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
    Online available: 10 December 2020

    Abstract884)   HTML1663)    PDF (2504KB)(453)      

    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.

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    Recent Advances and Future Outlook for Artificial Intelligence in Aquaculture | Open Access
    LI Daoliang, LIU Chang
    Smart Agriculture    2020, 2 (3): 1-20.   doi:10.12133/j.smartag.2020.2.3.202004-SA007
    Online available: 21 October 2020

    Abstract2831)   HTML6886)    PDF (2843KB)(3128)      

    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.

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    Distinguishing Volunteer Corn from Soybean at Seedling Stage Using Images and Machine Learning | Open Access
    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
    Online available: 12 October 2020

    Abstract1185)   HTML2621)    PDF (1967KB)(687)      

    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.

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    Identification and Morphological Analysis of Adult Spodoptera Frugiperda and Its Close Related Species Using Deep Learning | Open Access
    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
    Online available: 27 September 2020

    Abstract1090)   HTML793)    PDF (1962KB)(533)      

    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.

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    Application Analysis and Prospect of Nanosensor in the Quality and Safety of Agricultural Products | Open Access
    WANG Peilong , TANG Zhiyong
    Smart Agriculture    2020, 2 (2): 1-10.   doi:10.12133/j.smartag.2020.2.2.202003-SA003
    Abstract1003)   HTML1416)    PDF (1634KB)(524)      

    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.

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    Characteristics Analysis and Challenges for Fault Diagnosis in Solar Insecticidal Lamps Internet of Things | Open Access
    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
    Abstract1265)   HTML2473)    PDF (3592KB)(568)      

    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.

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    Airborne remote sensing systems for precision agriculture applications | Open Access
    Yang Chenghai
    Smart Agriculture    2020, 2 (1): 1-22.   doi:10.12133/j.smartag.2020.2.1.201909-SA004
    Abstract1838)   HTML7083)    PDF (1726KB)(1265)      

    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.

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    Indoor phenotyping platforms and associated trait measurement: Progress and prospects | Open Access
    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
    Abstract1877)   HTML5430)    PDF (1588KB)(1093)      

    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.

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    Research progress and developmental recommendations on precision spraying technology and equipment in China | Open Access
    He Xiongkui
    Smart Agriculture    2020, 2 (1): 133-146.   doi:10.12133/j.smartag.2020.2.1.201907-SA002
    Abstract1533)   HTML4456)    PDF (871KB)(1765)      

    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.

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    Progress and prospects of crop diseases and pests monitoring by remote sensing | Open Access
    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
    Abstract2360)   HTML7011)    PDF (566KB)(1554)      

    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.

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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 | Open Access
    Huang Yanbo
    Smart Agriculture    2019, 1 (4): 12-30.   doi:10.12133/j.smartag.2019.1.4.201909-SA003
    Abstract1286)   HTML2066)    PDF (1240KB)(848)      

    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.

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    Application analysis and suggestions of modern information technology in agriculture: Thoughts on Internet enterprises entering agriculture | Open Access
    Kong Fantao, Zhu Mengshuai, Sun Tan
    Smart Agriculture    2019, 1 (4): 31-41.   doi:10.12133/j.smartag.2019.1.4.201906-SA012
    Abstract2196)   HTML9913)    PDF (964KB)(1456)      

    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.

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    Information sensing and environment control of precision facility livestock and poultry farming | Open Access
    Teng Guanghui
    Smart Agriculture    2019, 1 (3): 1-12.   doi:10.12133/j.smartag.2019.1.3.201905-SA006
    Abstract1918)   HTML2510)    PDF (2880KB)(1659)      

    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.

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    Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing | Open Access
    Lan Yubin, Deng Xiaoling, Zeng Guoliang
    Smart Agriculture    2019, 1 (2): 1-19.   doi:10.12133/j.smartag.2019.1.2.201904-SA003
    Abstract3389)   HTML5881)    PDF (1165KB)(4799)      

    Rapid acquisition and analysis of crop information is the precondition and basis for carrying out precision agricultural practice. Variable spraying and agricultural operation management based on the actual degree of crop diseases, pests and weeds can reduce the cost of agricultural production, optimize crop cultivation, improve crop yield and quality, and thus achieve precise agricultural management. In recent years, with the rapid development of UAV industry, UAV agricultural remote sensing technologies have played an important role in monitoring crop diseases, insects and weeds because of high spatial resolution, strong timeliness and low cost. Firstly, this research introduces the basic idea and system composition of precision agricultural aviation, and the status of UAV remote sensing in precision agricultural aviation. Then, the common UAV remote sensing imaging and interpreting methods were discussed, and the progress of UAV agricultural remote sensing technologies in detecting crop diseases, pests and weeds were respectively expounded. Finally, the challenges in the development of UAV agricultural remote sensing technologies nowadays were summarized, and the future development directions of UAV agricultural remote sensing were prospected. This research can provide theoretical references and technical supports for the development of UAV remote sensing technology in the field of precision agricultural aviation.

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    Key technology analysis and research progress of UAV intelligent plant protection | Open Access
    Xu Min, Zhang Ruirui, Chen Liping, Tang Qing, Xu Gang
    Smart Agriculture    2019, 1 (2): 20-33.   doi:10.12133/j.smartag.2019.1.2.201812-SA025
    Abstract1677)   HTML2295)    PDF (1405KB)(1928)      

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

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    State-of-the-art and recommended developmental strategic objectivs of smart agriculture | Open Access
    Zhao Chunjiang
    Smart Agriculture    2019, 1 (1): 1-7.   doi:10.12133/j.smartag.2019.1.1.201812-SA005
    Abstract6273)   HTML16586)    PDF (433KB)(4668)      

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

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