Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs
Read the original on arXiv Computer Vision →EigenCL is a contrastive learning framework that stages crop stress by embedding Sentinel‑2 NDRE trajectories, producing four physiologically coherent clusters—Healthy, Mild, Moderate, and Severe—without retraining across different states. Trained on 10,000 maize NDRE patches from Iowa in 2020 and validated on Nebraska data in 2023, EigenCL outperformed baselines such as K‑Means, SimCLR, and ProtoCLR, achieving high silhouette, DBI, and CHI scores. The resulting clusters align with maize growth stages, correlate strongly with soil moisture and yield anomalies, and provide interpretable outputs like heatmaps and scouting priorities for decision‑support systems.
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