Early detection of crop stress is vital for minimizing yield loss and enabling timely intervention in precision agriculture. Traditional approaches using NDRE often detect stress only after visible symptoms appear or require labeled datasets, limiting scalability. This study introduces EigenCL, a novel unsupervised contrastive learning framework guided by temporal NDRE dynamics and biologically grounded eigen decomposition. Using over 10,000 Sentinel-2 NDRE image patches from drought-affected Iowa cornfields, we constructed five-point NDRE time series per patch and derived an RBF similarity matrix. The principal eigenvector explaining 76% of the variance and strongly correlated (r = 0.95) with raw NDRE values was used to define stress-aware similarity for contrastive embedding learning. Unlike existing methods that rely on visual augmentations, EigenCL pulls embeddings together based on biologically similar stress trajectories and pushes apart divergent ones. The learned embeddings formed physiologically meaningful clusters, achieving superior clustering metrics (Silhouette: 0.748, DBI: 0.35) and enabling 76% early stress detection up to 12 days before conventional NDRE thresholds. Downstream classification yielded 95% k-NN and 91% logistic regression accuracy. Validation on an independent 2023 Nebraska dataset confirmed generalizability without retraining. EigenCL offers a label-free, scalable approach for early stress detection that aligns with underlying plant physiology and is suitable for real-world deployment in data-scarce agricultural environments.