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

Abstract

Hyperspectral indices integrated with physiology predicted metabolites such as Rubisco activity across early, mid, and late flowering drought, establishing a rapid, non-destructive framework to detect sink limitations and identify cotton resilience to stage-specific stress and fiber quality decline.

Plant phenotyping relevance

綿花のスペクトル表現型と生理形質を機械学習で統合し、乾燥ストレス耐性や品質低下を非破壊・迅速に推定する枠組みが中心であるため。

Code and data availability

The supplied preprint blocks describe cotton drought phenotyping (PlantEye 3D, Resonon hyperspectral imaging, PLSR/SHAP machine learning) but contain no data availability statement, no public dataset deposit, and no code/model availability language or authors' public URL. The only URLs present are the preprint DOI and

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