Hyperspectral Grading of Rice Blast Severity (0–5) Using Regional Spectral-Angle Features and Heterogeneous Stacking (XGBSVMRF-SA)

Rice blast caused by Pyricularia oryzae progresses from symptom-free leaves to extensive tissue death, while neighboring severity grades often show similar spectra. This study developed XGBSVMRF-SA to integrate regional spectral-angle (SA) features with heterogeneous stacking of XGBoost 3.1.2, support vector machine, and random forest class probabilities. The analysis included 833 spectra from 40 recorded pot groups covering six severity grades (0–5). The 2151-band spectra underwent Savitzky–Golay smoothing and second-derivative preprocessing. Twelve near-infrared and shortwave-infrared SA features and 18 class probabilities formed a 30-dimensional logistic-regression input. Group-balanced grade centers and grouped out-of-fold predictions were generated within training partitions, without resampling or threshold adjustment. In matched grouped five-fold validation, XGBoost achieved 90.88% accuracy and 90.31% macro F1. Heterogeneous stacking increased these values to 91.34% and 90.61%; adding SA further increased them to 92.07% and 91.46%. The complete framework gained 1.19 and 1.16 percentage points over XGBoost, including SA-specific increments of 0.72 and 0.85 percentage points over the matched stack. Local perturbations of spectral-region boundaries produced a macro F1 span of 0.14 percentage points. These results support regional SA as a supplementary spectral-geometric input alongside class probabilities in heterogeneous stacking for quantitative rice blast grading.