I’m working on a hyperspectral dataset of cabbage crops for nitrogen deficiency detection. The dataset has 3 classes:
Healthy
Mild nitrogen stress
Severe nitrogen stress
I’m trying to use self-supervised learning (SSL) for representation learning and then fine-tune for classification.
What I’ve done:
Tried multiple SSL methods: BYOL, MAE, VICReg
Used data augmentation (spectral noise, masking, scaling, etc.)
Fine-tuned with a classifier head
Evaluated using accuracy and F1-score
Problem:
No matter what I try, the performance is stuck around:
Accuracy: ~45–50%
F1-score: also low (~0.5)
This is barely better than random (since 3 classes ≈ 33%).
My setup:
Hyperspectral data (hundreds of bands)
1D/patch-based model (ViT-style)
SSL pretraining → fine-tuning pipeline
Tried k-NN and linear probe as well (still weak)
What I suspect:
Classes might not be well separable spectrally
SSL methods designed for RGB may not adapt well
Augmentations might be hurting instead of helping
Model not capturing spectral-specific patterns
What I’m looking for:
Would really appreciate suggestions on:
Better SSL methods for hyperspectral data
Is VICReg actually the best choice here?
Should I try masked spectral modeling instead?
Feature engineering
Should I include vegetation indices (NDVI, etc.)?
PCA before training?
Model architecture
1D CNN vs ViT vs hybrid?
Any proven architectures for hyperspectral?
Evaluation
Best way to validate SSL representations?
Any tricks to improve linear probe results?
General advice
Anyone worked on plant stress / hyperspectral classification?
Common
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