Hyperspectral imaging (HSI) has rapidly advanced as a critical technology for agricultural applications, enabling precise, non-destructive detection of plant diseases, nutrient content, and product quality. This research synthesizes and highlights innovations in hardware systems, spectral range applications, artificial intelligence (AI) models, and real-world deployments. A hybrid generative augmentation approach that combines hard and soft spectral–spatial mixing is presented to improve intra-class variability and alleviate data scarcity. The result is synthetic exemplars that improve model generalization while maintaining discriminative class-specific characteristics. The pathogen-affected regions are segmented using a Gabor-modulated depth separable location attention network, where attention modules suppress background interference, depth-wise separable convolutions minimize computational overhead, and orientation- and frequency-selective Gabor filters highlight fine-grained texture distortions. A local triangular Hu binary pattern model is used for feature encoding, which works in concert with global invariant shape descriptors and local micro-textures to capture both with improved resilience to geometric changes. An equivariant spatial pyramid convolutional neural network (CNN), which makes use of multi-scale representations while preserving transformation consistency, is then used to accomplish classification. Kernel SHAP analysis improves model interpretability by clarifying the relative contributions of spectral–spatial characteristics to decision outcomes.