UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model

Rice leaf blast can develop rapidly under field conditions, creating a need for fast, non-destructive, and spatially explicit monitoring. UAV multispectral imagery and synchronized ground disease surveys were collected from artificially induced rice leaf blast experiments conducted in 2024 and 2025. After image registration, U-Net canopy segmentation, and ROI quality screening, 1801 curated 3 × 3-pixel canopy ROIs were retained. Each ROI was represented by four reflectance bands and 10 vegetation indices selected by Pearson correlation analysis, and the resulting dataset supported model development and spatial mapping. The improved 1DCNN-Transformer branch combined multi-scale Inception convolution, SE recalibration, and Focal Loss with RF and GBDT probability fusion. In the hold-out evaluation, the model achieved an overall accuracy of 98.90% and a weighted F1-score of 98.89%. Five repetitions of five-fold grouped cross-validation were performed, with identical 14-feature vectors constrained to the same fold. RF, GBDT, and equal RF-GBDT probability fusion achieved mean accuracies of 99.63%, 99.29%, and 99.33%, respectively, supporting strong class separability after duplicate-group isolation. The resulting severity and prescription maps provide an end-to-end digital workflow from canopy extraction to spatial decision support.