Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs
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 Iow
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- PossiblePossibly related (embedding) · 47%Regional drought prediction from Sentinel-2 time series using Random Forest, DNN, and 1D-CNN: a case study in Marchfeld, Austria - EurekAlert! →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
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- LinkedLinked via arxiv author · 85%Shafqaat Ahmad →
“Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outpu”