Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data  

作　　者：Li Song Luyuan Wang Zheqing Yang Li He Ziheng Feng Jianzhao Duan Wei Feng Tiancai Guo 

机构地区：[1]State Key Laboratory of Wheat and Maize Crop Science,Agronomy College,Henan Agriculture University,Zhengzhou 450046,Henan,China [2]Information and Management Science College,Henan Agricultural University,Zhengzhou 450046,Henan,China [3]CIMMYT-China Wheat and Maize Joint Research Center,State Key Laboratory of Wheat and Maize Crop Science,Henan Agricultural University,Zhengzhou 450046,Henan,China

出　　处：《The Crop Journal》2022年第5期1312-1322,共11页作物学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (31971791);the National Key Research and Development Program of China (2017YFD0300204)。

摘　　要：Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.

关 键 词：Characteristic wavelength selection Estimation model Machine learning Multi-angular remote sensing Wheat powdery mildew 

分 类 号：S435.121.46[农业科学—农业昆虫与害虫防治] S127[农业科学—植物保护]