农业机械学报2025，Vol.56Issue(5)：91-102,12.DOI:10.6041/j.issn.1000-1298.2025.05.009
基于光谱波段-纹理特征-植被指数融合的棉蚜虫危害等级无人机监测研究
UAV Monitoring of Cotton Aphid Damage Levels Based on Fusion of Spectral Bands,Texture Features and Vegetation Indices
摘要
Abstract
Accurate and nondestructive detection of cotton aphids is crucial for effective pest control and enhancing cotton yield and quality.Aiming to propose a multi-feature fusion method for cotton aphid damage level(CADL)monitoring,spectral feature wavelengths,vegetation indices,and cotton canopy texture characteristics were integrated to enhance the accuracy of cotton aphid damage level determination.A UAV-mounted hyperspectral imaging system was employed to collect hyperspectral image data of cotton canopy.Pre-processing of the extracted spectral data involved Savitzky-Golay smoothing(SG smoothing)and multiple scattering correction(MSC).Support vector machine(SVM)modeling was applied to the pre-processed spectral data,results revealed that MSC performed better than SG smoothing in pre-processing.Thus the spectral data pre-processed by MSC was used for characteristic wavelengths extraction.Characteristic wavelengths extraction was conducted by using the competitive adaptive reweighting algorithm(CARS)and the shuffled frog leaping algorithm(SFLA),totally 31 and 37 characteristic wavelengths were extracted by CARS and SFLA,respectively.Subsequently,the successive projections algorithm(SPA)was utilized for secondary characteristic wavelengths extraction.Ultimately,six sensitive wavelengths at wavelengths of 650 nm,786 nm,931 nm,938 nm,945 nm and 961 nm were extracted.Based on six secondarily extracted characteristic wavelengths,nine vegetation indices and eight texture features were calculated,followed by correlation analysis between these vegetation indices/texture features and CADL.Four machine learning models(LightGBM,XGBoost,SVM,RF)were developed to evaluate the classification performance by using characteristic wavelengths alone,vegetation indices alone,texture features alone,combined characteristic wavelengths and vegetation indices,and integrated characteristic wavelengths,vegetation indices,and texture features.Results indicated that vegetation indices(RDVI,SAVI,MSAVI,OSAVI)and texture features(MEA,VAR,DIS,HOM)exhibited strong correlations with CADL.The XGBoost model incorporating the tri-feature combination(characteristic wavelengths,vegetation indices,texture features)achieved optimal CADL classification performance,yielding an overall accuracy(OA)of 86.99％and a Kappa coefficient of 0.837 1 on the test set.Compared with models by using characteristic wavelengths alone,vegetation indices alone,texture features alone,or the dual-feature combination(characteristic wavelengths,vegetation indices),this integrated approach improved OA by 4.88,27.64,21.95,and 2.44 percentage points,respectively.关键词
棉蚜虫危害等级/航空遥感/高光谱/纹理特征/多特征融合Key words
cotton aphid damage levels/aerial remote sensing/hyperspectral/texture feature/multi-feature fusion分类
农业科技引用本文复制引用
廖娟,王辉,梁业雄,何欣颖,曾浩求,何松炜,唐赛欧,罗锡文..基于光谱波段-纹理特征-植被指数融合的棉蚜虫危害等级无人机监测研究[J].农业机械学报,2025,56(5):91-102,12.基金项目
国家重点研发计划项目(2022YFD2002400)、兵团财政科技计划项目(2023AB014)和国家自然科学基金项目(31901401) （2022YFD2002400）
