Monitoring of Winter Wheat Stripe Rust Based on Digital Camera and Multispectral Unmanned Aerial Vehicle

Tian Hongwei; Ji Xingjie; Tenzin Gregory; Ye Haotian; Zhao Zhiyu

doi:10.16765/j.cnki.1673-7148.2025.04.003

Abstract：For the quantitative monitoring and evaluation of winter wheat stripe rust,a disease index of wheat stripe rust extracted from canopy digital photos is taken as the dependent variable.By screening the remote sensing features from DJI Phantom 4 multiple spectral unmanned aerial vehicle(UAV)inclu-ding band reflectance,vegetation indices and GLCM(Gray-Level Co-occurrence Matrix)texture indices,and also comparing the simulation accuracy of eight machine learning algorithms under different combina-tions of remote sensing features,the optimal monitoring model of winter wheat stripe rust by multispectral UAV is confirmed.The results reveal that red channel digital number and the normalized red channel dig-ital number are the best indices to extract infected and normal green leaves from digital images,respec-tively.Among the five band reflectances,the reflectances of Near Infrared,Red edge,and Red channel are extremely significantly correlated to disease index.There are 29 out of 30 vegetation indices correlated to disease index at significant level or above,with the top 10 vegetation indices highly correlated being OSAVI,RBNDVI,PVI,SAVI,EVI2,NDVI,MSR,RVI,MNVI,and MTVI;Six texture indices from GLCM including Variance,Homogeneity,Contrast,Dissimilarity,Entropy,and Second Moment,are correlated to disease index at extremely significant level.The simulation test of eight machine learning al-gorithms of the four feature combination schemes shows that the ExtraTrees algorithm,which takes the band reflectance,vegetation indices and texture indices as remote sensing features,has the highest accu-racy,with a root mean square error of 1.2955,and is the optimal monitoring model for winter wheat stripe rust.