What question did this study set out to answer?

This study aims to identify how different flowering stages impact drought sensitivity and recovery in cotton.

March 28, 2026

Machine Learning Integrates Multispectral Phenotyping and Ionic Signatures to Reveal Stage‐Specific Drought Resilience in Cotton

Key Points

This study aims to identify how different flowering stages impact drought sensitivity and recovery in cotton.
Conducted controlled short-term droughts at early, mid, and late flowering stages.
Integrated multispectral and hyperspectral canopy phenotyping with physiological data.
Used explainable machine learning to identify spectral predictors of metabolic status and recovery.
Early and mid-flowering responses to drought were largely recoverable.
Late-flowering drought caused significant and irreversible losses in photosynthesis.
Canopy structure and fiber quality were most severely affected during late flowering.

Abstract

Summary statement Cotton ( Gossypium hirsutum ) drought sensitivity depends strongly on flowering stage, but stage‐resolved, non‐destructive detection remains limited. Using controlled short‐term droughts imposed at early, mid, or late flowering, we integrated multispectral and hyperspectral canopy phenotyping with physiology and explainable machine learning to identify spectral predictors of metabolic status and recovery. Early and mid‐flowering drought responses were largely recoverable, whereas late‐flowering drought caused the most potent and least reversible losses in photosynthesis, canopy structure, and fiber quality. These results highlight late flowering as a critical vulnerability window and provide a mechanistically grounded framework for rapid phenotyping of stage‐specific drought resilience.

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