农业与技术2026，Vol.46Issue(4)：71-77,7.DOI:10.19754/j.nyyjs.20260430013
无人机多光谱遥感监测稻瘟病的分级诊断模型研究
Research on Graded Diagnosis Model for Rice Blast Monitoring Based on UAV Multispectral Remote Sensing
摘要
Abstract
Rice blast is one of the most harmful diseases of rice cultivation,and it significantly reduces yields.The use of unmanned aerial vehicle(UAV)multispectral imaging technology holds considerable promise in terms of rapid and large-scale surveillance.In this study,a stratified diagnostic model for rice blast was developed using canopy multispectral information continuously collected by unmanned aerial vehicles(UAVs).The accuracy,precision,F1-score and recell of the stacking model were as high as 89.71％,89.29％,89.42％and 89.8％,respectively.During the study,12 vegetation indices(VIs)were sorted by correlation characteristics using a random forest to screen for the best combination of vegetation indices(VIs).The results showed that the three vegetation indices(VIs)of NDRE,NDVI and RVI were the most effective for the stratified diagnosis of rice blast throughout the growth period.The accuracy,precision,F1-score and recell of the stacking model were as high as 91.18％,90.34％,90.67％and 91.07％,respectively.This increased by 1.47％,1.05％,1.25％and 1.27％respectively.This study not only verified the validity of hierarchical diagnosis but also provided strong support for precise treatment,assisted fertilization and drug application,reduced environmental pollution and improved yield.关键词
无人机(UAV)/多光谱遥感/稻瘟病分级诊断/植被指数(VI)/机器学习Key words
unmanned aerial vehicle(UAV)/multispectral remote sensing/hierarchical diagnosis of rice blast/vegetation index(VI)/machine learning分类
农业科技引用本文复制引用
张占峰,陈科瑞,李芳宇,黄赓然,于佳永,刘剑,宋少忠..无人机多光谱遥感监测稻瘟病的分级诊断模型研究[J].农业与技术,2026,46(4):71-77,7.基金项目
中国铁塔股份有限公司吉林省分公司省内重点创新研发项目"基于多光谱成像的水稻病虫害AI识别监测技术"(项目编号:JLZH-2025-TT0011) （项目编号:JLZH-2025-TT0011）
