Monitoring of rubber tree powdery mildew by combining spatial-spectral features and plant traits quantified from UAV hyperspectral imagery

Le résumé fourni par la source

Powdery mildew is a major disease affecting rubber tree yield. Rapid and accurate monitoring of this disease is crucial for plantation management. Previous studies have focused on spectral and spatial data for monitoring powdery mildew but have not adequately addressed the underlying physiological and biochemical alterations induced by the disease. Therefore, this study proposes a method combining spatial-spectral features and plant traits to monitor rubber tree powdery mildew. Unmanned Aerial Vehicles (UAVs) equipped with the ULTRIS X20P hyperspectral sensor (350–1000 nm) were used to capture hyperspectral imagery in two rubber plantations. Plant traits (PTs), including chlorophyll (Cab), carotenoids (Car), anthocyanins (Anth), leaf water content (Cw), and dry matter content (Cm), were inverted from UAV hyperspectral imagery using a radiative transfer model. Meanwhile, spectral and spatial information in the imagery were analyzed to extract vegetation indices (VIs), texture features (TFs), and color features (CFs) sensitive to the disease. Machine learning algorithms, including Partial Least Squares Regression (PLSR), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest Regression (RF), were subsequently employed to create disease monitoring models based on these features. The results show that models based on VIs and TFs effectively monitor powdery mildew, with the inclusion of PTs significantly improving model performance. Models that integrate multiple features outperform those that depend on single features, especially the monitoring model integrating VIs, TFs, and PTs using the PLSR algorithm, which achieved an R 2 of 0.794 and an RMSE of 7.991. This study highlights the novelty of integrating spatial-spectral features with plant traits for monitoring rubber tree powdery mildew, offering a reference for precise disease monitoring through the use of UAV hyperspectral imagery.

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