A study uses FastGAN to generate 10,000 synthetic hyperspectral images of faba bean leaves, preserving real spectral and structural features. Transformer-based models, particularly Vision Transformer, achieve the highest accuracy and F1-scores in classifying healthy versus aphid-infested leaves, outperforming classical CNNs and demonstrating improved disease detection with reduced false negatives.