引用本文:赵倩,刘长斌,梅新,梅广源,陶婷,赵培钦,杨小冬.大田作物病害遥感监测技术及模型的研究现状与展望[J].中国农业信息,2024,36(1):12-30
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大田作物病害遥感监测技术及模型的研究现状与展望
赵倩1,2,刘长斌2,梅新1,梅广源1,2,陶婷1,2,赵培钦1,2,杨小冬2
1.湖北大学资源环境学院,武汉 430062;2.农业农村部农业遥感机理与定量遥感重点实验室/ 北京市农林科学院信息技术研究中心,北京 100097
摘要:
【目的】 高效准确监测大田作物病害对作物生产和粮食安全至关重要。文章旨在系统梳理大田作物病害遥感监测技术及模型的研究成果,推动作物病害监测技术发展与应用。【方法】 采用文献检索、归纳总结等方法,系统梳理了国内外大田作物病害遥感监测研究,阐述了大田作物病害遥感监测技术的未来发展趋势。【结果】 (1)阐述大田作物病害遥感监测基本原理,并构建了基本框架;(2)大田作物病害监测遥感数据源主要包括多光谱、高光谱、荧光和热红外遥感;(3)大田作物病害监测遥感模型主要包括统计模型、传统机器学习模型和深度学习模型。【结论】 未来应以病害早期监测、实时监测系统和数据共享为重点突破和研究的方向,为大田作物病害实时或准实时监测预测提供技术支撑。
关键词:  作物病害  遥感监测  反演模型  研究现状  未来展望
DOI:10.12105/j.issn.1672-0423.20240102
分类号:
基金项目:国家重点研发计划“全国粮食生产综合智能化大数据平台研发及应用示范”(2023YFD2000105)
Current status and prospects of remote sensing monitoring technology and models on field crop diseases:A review
Zhao Qian1,2, Liu Changbin2, Mei Xin1, Mei Guangyuan1,2, Tao Ting1,2, Zhao Peiqin1,2, Yang Xiaodong2
1.School of Resources and Environment,Hubei University,Wuhan,430062,Hubei,China;2.Key Laboratory of Agricultural Remote Sensing and Quantitative Remote Sensing Mechanism,Ministry of Agriculture and Rural Affairs,Information Technology Research Center/ Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China
Abstract:
[Purpose] The implementation of efficient and precise disease monitoring methods is crucial for ensuring food production and safety.This article aims to systematically review the research findings on remote sensing technology and models for monitoring diseases in field crops,thereby fostering the development and application of crop disease monitoring technology.[Method] The paper adopted the methods of literature retrieval and inductive summarisation to comprehensively review the research on remote sensing monitoring of crop diseases in domestic and foreign contexts,and elaborated the future development trend of remote sensing monitoring technology of field crop diseases.[Result] (1)The basic principles of remote sensing monitoring of crop diseases were elaborated and the basic framework was constructed;(2)The remote sensing data sources for crop disease monitoring mainly included multispectral,hyperspectral,fluorescence and thermal infrared remote sensing;(3)The remote sensing models for crop disease monitoring mainly included statistical models,traditional machine learning models and deep learning models.[Conclusion] In the future,early disease monitoring,real-time monitoring system and data sharing should be the key breakthroughs and research directions to provide technical support for real-time or quasi-real-time monitoring and prediction of field crop diseases.
Key words:  field crops  inversion models  future prospect  remote sensing monitoring