| 我国农作物病虫害智能监测预警技术新进展 |
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| 引用本文： | 封洪强,姚 青,胡 程,黄文江,胡小平,刘 杰,张云慧,张 智,乔红波,刘 伟.我国农作物病虫害智能监测预警技术新进展[J].植物保护,2023,49(5):229-242. |
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| 作者姓名： | 封洪强 姚 青 胡 程 黄文江 胡小平 刘 杰 张云慧 张 智 乔红波 刘 伟 |
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| 作者单位： | 1. 河南省0号昆虫雷达野外科学观测研究站, 河南省农作物病虫害防治重点实验室, 农业农村部华北南部作物有害生物综合治理重点实验室, 河南省作物保护国际联合实验室, 河南省农业科学院植物保护研究所, 郑州 450002; 2. 浙江理工大学计算机科学与技术学院, 杭州 310018; 3. 北京理工大学信息与电子学院雷达技术研究所, 北京 100081; 4. 中国科学院空天信息创新研究院, 北京 100094; 5. 旱区作物逆境生物学国家重点实验室, 植保资源与病虫害治理教育部重点实验室, 农业农村部西北黄土高原作物有害生物综合治理重点实验室, 西北农林科技大学植物保护学院, 杨陵 712100; 6. 全国农业技术推广服务中心, 北京 100125; 7. 中国农业科学院植物保护研究所, 植物病虫害综合治理全国重点实验室, 北京 100193; 8. 北京市植物保护站, 北京 100029; 9. 河南农业大学信息与管理科学学院, 郑州 450046 |
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| 基金项目： | 国家自然科学基金（32072414）; 国家重点研发计划（2022YFD1400403）; 河南省重大科技专项（201300111500）; 河南省农业科学院创新团队专项（2023TD13） |
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| 摘 要： | 近年来, 随着计算机、互联网、物联网、人工智能、传感器、遥感等技术的快速发展, 智能虫情测报灯、智能性诱捕器、昆虫雷达、无人机遥感、卫星遥感、智能识别App等现代智能农作物病虫监测装备及重大病虫害实时智能监测预警系统建设方面取得了比较明显的进步。本文综述了我国近5年在利用光谱遥感、昆虫雷达、图像识别等技术进行农作物病虫害监测预警研究和应用方面取得的重要进展, 分析了各类技术存在的不足与难点, 提出了未来发展的方向, 以期为充分利用空天地多源数据实现农作物病虫害精准预报提供指导。
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| 关 键 词： | 农作物病虫害 监测预警 光谱 卫星遥感 无人机 昆虫雷达 智能虫情测报灯 |
| 收稿时间： | 2023/8/4 0:00:00 |
| 修稿时间： | 2023/8/30 0:00:00 |
Recent advances in intelligent techniques for monitoring and prediction of crop diseases and insect pests in China |
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| FENG Hongqiang,YAO Qing,HU Cheng,HUANG Wenjiang,HU Xiaoping,LIU Jie,ZHANG Yunhui,ZHANG Zhi,QIAO Hongbo,LIU Wei.Recent advances in intelligent techniques for monitoring and prediction of crop diseases and insect pests in China[J].Plant Protection,2023,49(5):229-242. |
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| Authors: | FENG Hongqiang YAO Qing HU Cheng HUANG Wenjiang HU Xiaoping LIU Jie ZHANG Yunhui ZHANG Zhi QIAO Hongbo LIU Wei |
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| Institution: | 1. Entomological Radar Station Zero of Henan Province for Field Scientific Observation and Research, Henan Key Laboratory for Control of Crop Diseases and Insect Pests, Key Laboratory of Integrated Pest Management on Crops in Southern Region of North China, Ministry of Agriculture and Rural Affairs, International Joint Research Laboratory for Crop Protection of Henan, Institute of Plant Protection, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; 2. College of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; 3. Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China; 4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 5. State Key Laboratory of Crop Stress Biology for Arid Areas, Key Laboratory of Plant Protection Resources and Pest Management of Ministry of Education, Key Laboratory of Integrated Pest Management on the Loess Plateau of Ministry of Agriculture and Rural Affairs, College of Plant Protection, Northwest A & F University, Yangling 712100, China; 6. National Agro-Tech Extension and Service Center, Beijing 100125, China; 7. State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China; 8. Beijing Plant Protection Station, Beijing 100029, China; 9. College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China |
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| Abstract: | In recent years, with the rapid development of computer, internet, internet of things, artificial intelligence, sensor, remote sensing and other technologies, significant progress has been made in the construction of modern intelligent equipment for crop disease and insect pest monitoring, such as intelligent light-trap, intelligent pheromone trap, entomological radar, UAV remote sensing, satellite remote sensing, intelligent identification App, and real-time monitoring and early warning systems for major plant diseases and pests. In this paper, the important technical progresses and application cases in monitoring and forecast of crop pests and diseases with spectral remote sensing, entomological radar and image recognition in China in the past five years are reviewed, the shortcomings and challenges of various technologies are analyzed, and the direction of future development is proposed, in order to provide a guidance for the accurate forecast of crop pests and diseases by making full use of multi-source data from space, sky and earth surface. |
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| Keywords: | crop diseases and pests monitoring and forecast spectrum satellite remote sensing UAV entomological radar intelligent light-trap |
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