Summary
A researcher is struggling to achieve satisfactory accuracy (~50%) using various self-supervised learning (SSL) methods like BYOL, MAE, and VICReg on a hyperspectral dataset for detecting nitrogen deficiency in cabbage crops. Despite employing data augmentation and fine-tuning, the models are underperforming across three classes (Healthy, Mild, Severe stress), indicating a challenge in applying current SSL techniques effectively to this specific agricultural imaging task.
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