基于无人机多光谱影像的棉花黄萎病监测  

Monitoring of cotton Verticillium wilt based on unmanned aerial vehicle multispectral images

作　　者：宋勇 陈兵[1] 王琼[1] 王刚[1] 王静[4] 刘海建[3] 郑登科 李金霞 陈子杰 孙乐鑫 Song Yong;Chen Bing;Wang Qiong;Wang Gang;Wang Jing;Liu Haijian;Zheng Dengke;Li Jinxia;Chen Zijie;Sun Lexin(Cotton Institute,Xinjiang Academy of Agricultural and Reclamation Science,Shihezi,Xinjiang 832000,China;Shihezi University,Shihezi,Xinjiang 832003,China;Yumen Agricultural Technology Service Center,Yumen,Gansu 735200,China;Water Conservancy Construction Engineering Branch,Xinjiang Shihezi Vocational Technical College,Shihezi,Xinjiang 832003,China)

机构地区：[1]新疆农垦科学院棉花研究所,新疆石河子832000 [2]石河子大学,新疆石河子832003 [3]玉门市农业技术服务中心,甘肃玉门735200 [4]新疆石河子职业技术学院水利建筑工程分院,新疆石河子832003

出　　处：《棉花学报》2023年第2期87-100,共14页Cotton Science

基　　金：国家自然科学基金(41961054);新疆农垦科学院智慧棉田创新团队(NCG202304);新疆生产建设兵团英才项目(BTYC202001)。

摘　　要：【目的】通过无人机多光谱影像监测棉田黄萎病发病情况,为棉花黄萎病的精准防控提供理论指导。【方法】通过分析黄萎病发病棉田冠层的光谱特征,筛选无人机多光谱影像识别棉花黄萎病的最佳植被指数、最佳波段组合及最佳时相,并基于筛选的最佳时相建立黄萎病不同发病程度的棉田影像图,利用平行六面体法、最大似然法及支持向量机径向基函数分类法对影像图进行分类对比和精度评价。【结果】结果表明,在710~760nm波段,不同发病程度的棉株冠层光谱反射率均随着波长的增加明显增加;在760~950 nm波段,棉株冠层光谱反射率随着黄萎病的加重明显减小。随着黄萎病加重发生,棉株的叶片叶绿素含量、地上部鲜物质质量、地上部干物质质量、植株含水量以及叶面积指数均降低。无人机多光谱遥感识别棉花黄萎病的最佳植被指数和最佳波段组合分别是差值植被指数(difference vegetation index,DVI)和B_(3-5-8)(对应的波长分别为550 nm、656 nm和800 nm)。8月中下旬为无人机多光谱遥感识别棉花黄萎病发生程度的最佳时相。支持向量机径向基函数分类法结合最佳波段组合B_(3-5-8)与DVI综合影像对棉田黄萎病病情的分类精度最高(分类精度为96.64%,Kappa系数为95.61%)。棉田黄萎病发病程度的分类结果与棉株冠层光谱反射率和棉株农学参数的变化相对应,并与实地调查结果一致。【结论】利用支持向量机径向基函数分类法、最佳波段组合B_(3-5-8)与DVI综合影像对棉田黄萎病发病情况进行分类是可行的,研究结果可为利用遥感技术监测作物类似病虫害提供理论依据。[Objective]The unmanned aerial vehicle(UAV)multi-spectral remote sensing technology was used to monitor the severity of cotton Verticillium wilt,which will provide theoretical guidance for the precise prevention and control of cotton Verticillium wilt.[Method]By analyzing the spectral characteristics of canopy in cotton field affected by Verticillium wilt,the best vegetation index,the best wavelength combination and the best time phase for multi-spectral identification of Verticillium wilt by UAV were selected.The images of cotton fields differing in the severity of Verticillium wilt were established based on the optimal time phase.Parallelepiped method,maximum likelihood method and support vector machine radial basis function classification method were used to classify and evaluate the accuracy of the images.[Result]The results showed that the canopy spectral reflectance of cotton plants differing with occurrence of Verticillium wilt increased obviously with the increase of wavelength at 710-760 nm,the spectral reflectance of cotton canopy decreased obviously with the aggravation of Verticillium wilt at the wavelength of 760-950 nm.With the aggravation of Verticillium wilt,the chlorophyll content of leaves,fresh mass of aerial tissue per plant,dry mass of aerial tissue per plant,plant water content and leaf area index of cotton plants were all decreased.The best vegetation index and best band combination for UAV multi-spectral remote sensing to identify cotton Verticillium wilt were difference vegetation index(DVI)and B_(3-5-8)(the corresponding wavelengths are 550 nm,656 nm and 800 nm).Mid to late August was the best time to identify the occurrence degree of cotton Verticillium wilt by UAV multi-spectral remote sensing.Support vector machine radial basis function classification method,best band combination B_(3-5-8) and DVI integrated image had the highest classification accuracy for Verticillium wilt in cotton field(classification accuracy was 96.64%,and Kappa coefficient was 95.61%).The classification results

关 键 词：棉花 黄萎病 无人机 多光谱影像 分类方法 

分 类 号：S435.621[农业科学—农业昆虫与害虫防治] S127[农业科学—植物保护]