Estimating canopy chlorophyll content of powdery mildew stressed winter wheat by different spatial resolutions of UAV-imagery

Abstract

The wheat powdery mildew (WPM) always alters the pigment and structure of the leaf and canopy, disrupting crop growth. A challenge on the WPM monitoring is the limited capability of unmanned aerial vehicle (UAV)-based canopy images to directly indicate complex infection symptoms. However, the WPM infection markedly changed canopy chlorophyll content (CCC), which encompassed both leaf and canopy attributes, and this change was relatively easy to capture by UAV remote sensing. Thus, this study aimed to estimate CCC to indirectly explore WPM using different scales of UAV image features. UAV-based winter wheat canopy images were acquired continuously in the field during the early, middle, and late infection stages after being artificially inoculated with fungal pathogens at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China, in 2022. The study evaluated the potential of spectral (Spe) and textural (Tex) features and their combination to estimate CCC and characterize WPM dynamic. Considering the impacts of spatial scales, the selected Spe and Tex textures were calculated from images with 1, 2, 5, 10, 15, and 20 cm spatial resolution. The changes in different types of features under WPM stress and their response to CCC were analyzed. Three regression methods, including extreme gradient boosting regression (XGBR), multilayer perceptron regression (MLPR), and partial least squares regression (PLSR) were used to estimate CCC based on the acquired sensitive features and track the infection status. Results showed that the image spatial resolution barely affected the Spe performance while notably affecting the Tex performance. The performance of estimating CCC under WPM stress was superior for Tex (ranging from 1 to 20 cm spatial resolution imagery) compared to Spe features. The best modeling result was the combination of Spe with Tex features from 1 and 10 cm (R² = 0.82, RMSE = 28.49 mg/L, NRMSE = 12.38 %), which could be related to information captured from different viewpoints. Although finer spatial resolution was advantageous for capturing the complex symptoms caused by WPM, it increased the burden on UAV missions. UAV multispectral imagery with the 10 cm spatial resolution using XGBR (R² = 0.74, RMSE = 33.48 mg/L, NRMSE = 14.55 %) might be used as an optimization scheme for estimating CCC and exploring WPM stress, as it decreased the cost associated with data processing and time in the actual operation. This study indirectly characterizes the condition of WPM infection by estimating CCC, which provides promising and valuable insights for disease management and control in the field.