Deep learning–driven hyperspectral imaging for drought stress detection in dragoon lettuce for space production

Abstract

Sustainable plant cultivation is critical for supporting long-duration space missions by ensuring reliable food production in extraterrestrial environments where resources are severely limited and growth systems operate in closed-loop conditions. With crew members managing multiple critical mission tasks and having minimal time for plant care, autonomous stress detection systems must provide reliable, interpretable diagnostics to enable rapid, informed decision-making for crop management. This study utilized a custom hyperspectral imaging (HSI) system designed for space applications to develop an AI-driven diagnostic framework. We propose a novel SAM-ViT-3PE architecture that uniquely combines sparse spectral band selection with 3D spatial-spectral patch embedding, preserving rich spatial-spectral information typically lost in conventional ROI-averaged approaches. A key temporal finding identified Day 3 After Treatment (DAT 3) as the critical threshold where drought stress signatures become distinctly detectable, with accuracy dramatically improving from 72.2% to 95.9%. By focusing analysis on data from DAT 3 onward, the SAM-ViT-3PE model achieved superior performance compared to traditional ML methods and standard deep learning approaches, with accuracy of 95.4%, precision of 96.6% and recall of 94.1%. Furthermore, Explainable AI using Integrated Gradients enabled interpretable diagnostics through physiologically meaningful spectral bands and spatial stress patterns. These results demonstrate that the AI-enhanced HSI framework provides both high-accuracy autonomous detection and scientifically grounded interpretability essential for trustworthy crop management in resource-constrained space environments.