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

Powdery mildew is a disease that threatens wheat production and causes severe economic losses world-wide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multi-angle 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 vari-ables 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 predic-tion 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 wave-length 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 (R2) single-peaked at different observation angles. The con-structed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R2 > 0.8 at each measured angle. Especially for larger angles, monitoring accuracies were increased relative to the optimal VI model (40% at -60 degrees, 33% at +60 degrees), indi-cating that the CARS-ELM model is suitable for extreme angles of -60 degrees and +60 degrees. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.(c) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).