Early Detection of Water Stress by Plant Electrophysiology: Machine Learning for Irrigation Management

TL;DR

This study presents a machine learning framework that detects early water stress in tomato plants using electrophysiological signals, enabling timely irrigation decisions to optimize resource use.

Contribution

It introduces a novel electrophysiology-based stress detection method with high accuracy, outperforming deep learning, and demonstrates its potential for automated irrigation management.

Findings

30-minute look-back window balances speed and accuracy

Automated machine learning achieves up to 92% classification accuracy

Framework detects stress transitions in unseen data

Abstract

Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. Methods: In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The recorded time-series data were processed using a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Results: Across multiple input time horizons, we found that a 30-minute look-back window strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Importantly, the framework detects transitions from healthy to stressed states in recordings that were not included in the training set. Conclusion: Overall, we provide a decision-support tool for farmers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.