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This study introduces EigenCL, a physiology-guided contrastive learning framework that stages crop stress using NDRE trajectories from Sentinel-2 imagery, addressing the limitations of traditional vegetation indices. Trained on 10,000 maize NDRE patches from drought-affected Iowa fields, EigenCL was tested on Nebraska fields without retraining, achieving superior performance over baseline methods by producing four coherent stress clusters that align with maize growth stages. The model's outputs not only correlate with soil moisture and yield anomalies but also provide interpretable diagnostics for decision support systems, enhancing early detection of crop stress in the context of climate change.
EigenCL reveals that embedding NDRE trajectory dynamics into contrastive learning can significantly enhance the accuracy and interpretability of crop stress diagnostics.
Timely detection of crop stress is critical for sustaining yields under increasing drought frequency, yet conventional vegetation index thresholds or image-based clustering often fail to capture stress progression, limiting their value for farm decision-making. To address this gap, we present EigenCL, a physiology-guided contrastive learning framework that stages crop stress from Sentinel-2 NDRE trajectories, with the goal of providing interpretable and transferable stress diagnostics for decision support systems (DSS). EigenCL was trained on 10,000 maize NDRE patches from drought-affected Iowa fields in 2020 and tested on Nebraska fields in 2023 without retraining, with validation incorporating soil-moisture records, U.S. Drought Monitor maps, and county-level yield statistics. The model produced four physiologically coherent stress clusters (Healthy, Mild, Moderate, Severe), significantly outperforming baselines including K-Means, SimCLR, ProtoCLR, and an ablation model (Silhouette = 0.748, DBI = 0.35, CHI = 49,624). Clusters aligned with maize growth stages, with severe stress peaking around tasseling-silking (VT-R1), a stage known to drive yield loss; moreover, EigenCL clusters correlated with soil moisture at 0-14-day lags (rho up to 0.72) and matched yield anomalies in drought-affected counties. By embedding NDRE trajectory dynamics into contrastive learning, EigenCL enables early stress alerts and interpretable DSS outputs (e.g., heatmaps, scouting priorities, regional risk indices), extending beyond single-date NDRE thresholds and supporting scalable monitoring for climate-smart agronomy.
