The accuracy plateau you are hitting is not a failure of effort. It is a signal that the problem is being framed around the wrong assumptions. Stuck at 45 to 50 percent with three classes, you are barely above random, which means the model is not learning a meaningful separation between mild and severe nitrogen stress. That is not a tuning issue. It is a representational one.
Your suspicion about the data itself is the most credible lead. Hyperspectral bands are not RGB channels, and the augmentations that help a Vision Transformer on ImageNet can actively destroy the subtle spectral signatures you are trying to isolate. Adding spectral noise or random masking might be washing out the very gradients that separate healthy from mildly stressed tissue. If the classes are not spectrally distinct to begin with, no SSL method will conjure structure that is not there. Before you swap BYOL for VICReg again, run a basic PCA and look at the cluster separation in two or three dimensions. If the healthy and mild classes overlap heavily in that reduced space, the problem is the data or the label definition, not the pretraining objective.
On the SSL method itself, masked spectral modeling is worth trying before you abandon the approach entirely. MAE-style masking forces the model to reconstruct the full spectrum from a partial view, which can push it to learn the continuous structure of reflectance curves. VICReg and BYOL are designed for invariance to augmentations, but if your augmentations are not biologically grounded, you are teaching the model to ignore the exact features that matter. A simple 1D CNN with a small patch size may also outperform a ViT here, because hyperspectral data is high-dimensional but locally smooth, and CNNs impose a strong inductive bias that suits that structure. Your linear probe results being weak is consistent with the learned representations being too generic or too distorted by augmentation.
Do not add vegetation indices yet. That is a band-aid, not a fix. If NDVI or similar indices were necessary for separation, you would likely see some improvement in a simple classifier trained on raw bands first. Your priority should be to establish a baseline with a fully supervised 1D CNN on raw spectra, no SSL, no augmentation beyond minimal normalization. If that baseline also lands near 50 percent, then the labels are noisy or the stress levels are not spectrally distinct in your acquisition setup. If the supervised baseline clears 70 percent, then your SSL pipeline is actively destroying information, and you should focus on reducing augmentation intensity and simplifying the pretraining task. Validate with a holdout set and report confidence intervals, because with this kind of variance, a few lucky seeds can mislead you for weeks. Stop chasing methods and start interrogating the data.