Stage-specific drought resilience in cotton revealed by integrating machine learning, physiological traits, spectral phenotyping, and ionomic signatures

Authors: MALICK CISSE, EL-HADJI - Oak Ridge Institute For Science And Education (ORISE); GAJANAYAKE, BANDARA - Oak Ridge Institute For Science And Education (ORISE); MATHUR, SONAL - Oak Ridge Institute For Science And Education (ORISE); Chang, Christine; Fleisher, David; Fultz, Lisa; Timlin, Dennis; MITRA, ALAKANANDA - University Of Nebraska; Reddy, Vangimalla

Submitted to: Plant Cell and Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/24/2026
Publication Date: N/A
Citation: N/A

Interpretive Summary: The U.S. cotton industry produces over an annual $21 billion in products and services while supporting over 125,000 jobs. While cotton generally tolerates periods of drought during its growth cycle, water shortages during critical flowering periods can drastically reduce yield and lower fiber quality, directly impacting farmer income and regional economies. This study closely examined how the occurrence of drought at different stages of flowering (early, mid, and late) specifically affects cotton plant physiology and production. By using advanced imaging and computer modeling technologies, we tracked plant health, nutrient balance, and fiber quality in controlled environment experiments. Our findings show that drought occurring later in flowering causes severe and lasting damage, notably reducing the strength and overall quality of cotton fibers. Recovery of the cotton plant after drought exposure can lessen the severity of these impacts. However, our results showed recovery was uneven and depends significantly on when the drought stress occurs. This research provides valuable guidance that can help farmers protect their crops, stabilize cotton yields, and maintain the economic growth of cotton-producing communities.

Technical Abstract: Cotton is known for its drought tolerance and ability to sustain fiber production under water-limited conditions. However, the specific impact of drought during flowering remains unknown despite its critical influence on cotton physiology, yield, and fiber quality. An experiment was conducted to assess the flowering stage-specific drought and recovery responses of cotton by imposing 10-day drought treatments at early, mid, and late flowering stages, followed by rewatering and monitoring of recovery. A comprehensive time-series dataset was obtained which encompassed spectral and physiological indices, lint quality metrics, and ionomic profiles, from which we mapped cotton varietal trait trajectories across different flowering phases. The spectral shifts from green and near-infrared dominance toward higher anthocyanin reflectance and senescence-associated bands showed developmental stage-specific patterns. These spectral disruptions mirrored an intense physiological decline in Rubisco activity and net photosynthesis particularly during late-stage stress, with only partial trait recovery post-drought. Based on machine learning classifiers and t-SNE clustering, physiological parameters provided a better homogenous cluster between drought and recovery across stages than spectral imaging. Partial least squares regression models achieved high accuracy in predicting Rubisco activity through hyperspectral imaging. Fiber quality traits such as strength and elongation also showed the lowest resilience scores under late-stage drought. Temporal trait clustering and ternary balance modeling illustrated that recovery dynamics are non-linear and trait dependent. Our results uniquely define a spectral–physiological architecture of cotton drought resilience, offering trait-based targets and predictive frameworks for stage-specific stress adaptation in crops.