Self-supervised learning barely works on hyperspectral crop data (50% accuracy) — what's going wrong?

You're training an AI model to detect nitrogen deficiency in cabbage crops using hyperspectral imaging (basically fancy cameras that see beyond what human eyes can). You've got three classes to identify: healthy plants, mildly stressed plants, and severely stressed plants. You tried using self-supervised learning methods (BYOL, MAE, VICReg) — these are techniques where the AI learns patterns from unlabeled data first, then you fine-tune it for your specific task. Sounds smart in theory, but you're only getting ~50% accuracy, which is basically a coin flip. You've already tried the usual tricks: data augmentation, different SSL methods, proper fine-tuning. The problem is likely one of these: your hyperspectral data might be too different from what these SSL methods were designed for, you might need domain-specific augmentations that actually make sense for spectral data, or the pre-training setup itself isn't capturing what matters for crop stress. Worth checking if supervised learning baseline performs better, and whether your augmentations are actually helping or just adding noise.