现代化技术在森林病虫害监测与预警中的研究进展

doi:10.13304/j.nykjdb.2023.0210

摘要/Abstract

摘要：

森林对生态环境的保护和经济的发展起重要作用，然而病虫害的侵染严重制约了森林资源的可持续发展。近年来，遥感、机器视觉、生物传感器、物联网等现代化监测技术迅速发展，为森林大面积病虫害的精准监测与快速预警奠定了坚实基础。因此，就现代化技术在森林病虫害监测和预警方面的应用进行综合评述，旨在为相关从业者提供技术参考及辅助决策依据。在遥感方面，介绍了基于光谱响应监测森林病虫害的机理，从近地、地块及区域3个尺度对森林病虫害遥感监测的研究现状进行总结和讨论；在机器视觉方面，对比传统图像处理方法与深度学习的优缺点，从迁移学习、轻量化模型等方面分析提高监测效率的可行性；在生物学方面，阐述了如何基于虫类的生物学特征以及植物的生物学变化实现对病虫害的监测。此外，对物联网、5G等网络技术与现代监测技术相结合的方法进行探讨，以期达到对森林病虫害进行远程监控与预警的目的。最后，针对现阶段森林病虫害监测不及时、演变不清晰、预警不准确、防治不精准等问题，提出今后亟需以物联网技术为核心，建立地面、空中立体化病虫害监测网络，构建完备的病虫害数据库，建立多终端在线实时信息显示的监测和预警系统。

关键词: 森林病虫害, 遥感, 机器视觉, 生物学, 物联网, 监测预警

Abstract:

Forestry plays an important role in ecological environment protection and sustainable economic development， and forestry diseases and insect pests are the main factors restricting forestry development. In recent years， with the development of modern technologies such as remote sensing， machine vision， biology and the internet of things， an important means has been provided for accurate， rapid and large-scale forest pest monitoring and early warning. Therefore， a comprehensive review was made on the application of modern technology in forest pest monitoring and early warning， aiming at providing technical reference and auxiliary decision basis for relevant practitioners. In terms of remote sensing， the mechanism of forest pest monitoring based on spectral response was introduced， and the latest application progress of remote sensing technology in forest pest monitoring was discussed from different scales. In the aspect of machine vision， the advantages and disadvantages of traditional machine learning and deep learning were compared， and the help of transfer learning to network training and the feasibility of lightweight model in improving monitoring efficiency were analyzed. In biology， how to use the biological characteristics of insects and biological changes in plants to monitor insect pests and diseases were analyzed. On this basis， the combined application of internet of things， 5G and other network technologies with modern monitoring technology was expounded for remoting monitoring and early warning of forest pests and diseases. Finally， in view of the current forest disease and pest monitoring was not timely， the evolution was not clear， the early warning was not accurate， the prevention and control was not accurate and other problems， it should be urgent to establish the ground and air three-dimensional pest monitoring network with the internet of things technology as the core in the future， build a complete pest and disease database， and establish a multi-terminal online real-time information display monitoring and early warning system.

Key words: plant diseases and insect pests, remote sensing, machine vision, biology, internet of things, monitoring and warning

中图分类号:

蒋雪松, 戎子凡, 黄林峰, 陈青, 贾志成, 王金鹏. 现代化技术在森林病虫害监测与预警中的研究进展[J]. 中国农业科技导报, 2025, 27(1): 1-16.

Xuesong JIANG, Zifan RONG, Linfeng HUANG, Qing CHEN, Zhicheng JIA, Jinpeng WANG. Research Progress on Monitoring and Early Warning Technology of Forestry Pests and Diseases[J]. Journal of Agricultural Science and Technology, 2025, 27(1): 1-16.

图/表 8

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病虫害 Plant disease and insect pest	光谱指标 Spectral index	公式 Equation	参考文献Reference
松枯萎病 Bursaphelenchus xylophilus nickle	归一化植被指数NVDI	（R_NIR-R_R）/（R_NIR+R_R）	［44］
	不同指数比Different index ratio CDIR	R₇₆₀/R₆₇₅	［44］
	归一化绿红差异指数NGRDI	（R_G-R_R）/（R_G+R_R）	［50］
	△NGRDI	NGRDI₂-NGRDI₁	［50］
火疫病 Fire blight disease	QF1 1450	（R₁₆₀₀-R₁₄₅₀）/（R₁₆₀₀+R₁₄₅₀）	［7］
	QF2 1910	（R₁₆₀₀-R₁₉₁₀）/（R₁₆₀₀+R₁₉₁₀）	［7］
	比值植被指数RVI	R_NIR/R_R	［21］
	花青素反射率指数ARI	（1/R₅₅₀）-（1/R₇₀₀）
	三角植被指数TVI	0.5（120R₇₅₀-R₅₅₀）-200（R₆₇₀-R₅₅₀）
黄叶病 Chlorotic disorder	植物衰老反射指数PSRI	（R₆₇₈-R₅₅₀）/R₇₅₀	［43］
黄叶病 Chlorotic disorder	增强植被指数EVI	2.5（R_NIR-R_R）/（R_NIR+6R-7.5R_B+1）	［43］
小蠹虫虫害 Silverfish infestation	增强湿度差值指数Enhanced humidity difference index （EWDI）	Wetness₂-Wetness₁	［38］
	温度植被干旱指数Temperature vegetation dryness index （TVDI）	（T_S-T_SMIN）/（T_SMAX-T_SMIN）	［41］
	归一化植被指数NVDI	（R_NIR-R_R）/（R_NIR+R_R）	［48］

病虫害 Plant disease and insect pest	光谱指标 Spectral index	公式 Equation	参考文献Reference
松枯萎病 Bursaphelenchus xylophilus nickle	归一化植被指数NVDI	（R_NIR-R_R）/（R_NIR+R_R）	［44］
	不同指数比Different index ratio CDIR	R₇₆₀/R₆₇₅	［44］
	归一化绿红差异指数NGRDI	（R_G-R_R）/（R_G+R_R）	［50］
	△NGRDI	NGRDI₂-NGRDI₁	［50］
火疫病 Fire blight disease	QF1 1450	（R₁₆₀₀-R₁₄₅₀）/（R₁₆₀₀+R₁₄₅₀）	［7］
	QF2 1910	（R₁₆₀₀-R₁₉₁₀）/（R₁₆₀₀+R₁₉₁₀）	［7］
	比值植被指数RVI	R_NIR/R_R	［21］
	花青素反射率指数ARI	（1/R₅₅₀）-（1/R₇₀₀）
	三角植被指数TVI	0.5（120R₇₅₀-R₅₅₀）-200（R₆₇₀-R₅₅₀）
黄叶病 Chlorotic disorder	植物衰老反射指数PSRI	（R₆₇₈-R₅₅₀）/R₇₅₀	［43］
黄叶病 Chlorotic disorder	增强植被指数EVI	2.5（R_NIR-R_R）/（R_NIR+6R-7.5R_B+1）	［43］
小蠹虫虫害 Silverfish infestation	增强湿度差值指数Enhanced humidity difference index （EWDI）	Wetness₂-Wetness₁	［38］
	温度植被干旱指数Temperature vegetation dryness index （TVDI）	（T_S-T_SMIN）/（T_SMAX-T_SMIN）	［41］
	归一化植被指数NVDI	（R_NIR-R_R）/（R_NIR+R_R）	［48］

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