Drought-induced early alterations in photosynthetic efficiency revealed by convergence of spectral and molecular evidence

Mounting climate volatility, characterized by increasingly frequent and severe events, poses a critical threat to global food security. Traditional irrigation methods, which react only to visible drought symptoms, often fail to prevent irreversible physiological damage to crops. This underscores the need for precise, early detection of sub-lethal plant stress—a core challenge for precision agriculture. Effective early warning would enable proactive, smart irrigation, optimizing water use while protecting crop yields in a changing climate. Current drought assessment methods face significant trade-offs. Direct physiological measurements, though accurate, are destructive and impractical for field-scale use. Hyperspectral imaging (HSI) offers a non-destructive alternative by capturing detailed reflectance spectra. While it has identified signatures of advanced drought stress, a critical gap still remains, reliably predicting the initial metabolic perturbations that precede visible decline, particularly the early drop in net photosynthetic assimilation (An) which is a sensitive indicator of plant metabolic function and stress tolerance.

Our research directly addresses this need. Through controlled drought experiments of model plant Arabidopsis thaliana, we simultaneously collected high-resolution HSI data, transcriptome profiles, and ground-truth An measurements. A partial least squares regression model trained on spectral features accurately predicted An values two days in advance. Feature analysis identified wavelengths near 700 nm within the red-edge and near-infrared transition, as optimal early predictors. Strikingly, transcriptome data revealed a concurrent increase in gene activity linked to red and far-red light response pathways in drought-stressed plants. This convergence of spectral and molecular evidence indicates that early drought-induced photosynthetic alterations, predictive of An decline, manifest in canopy reflectance at ~700 nm and are underpinned by specific light-responsive molecular changes. By integrating hyperspectral phenotyping with mechanistic transcriptomics, we bridge prediction and biological causality, transforming HSI from a correlative tool into a mechanistically grounded early-warning system. This approach enables proactive, physiologically informed water management, paving the way for more climate-resilient agriculture.