Water status assessment in grapevines using plant electrophysiology

Abstract

Traditional methods for assessing vine water status, such as the Scholander pressure chamber, are time-consuming, punctual and labour-intensive. The development of alternative methods which are accurate, reliable and can provide real-time information on vine water status is a necessity for farmers all over the world. This study proposes the use of plant electrophysiology as a novel approach for real-time water status assessment in grapevines. We conducted four climate chamber experiments with potted grapevines under different irrigation regimes. Various morphological and physiological assessments were performed in parallel with electrophysiological measurements to correlate classic water status assessment methods with plant electrophysiological signals. Two machine learning approaches based on classification and regression were employed to train the prediction models. Results obtained from both models indicate significant differences in irrigation status between well-watered and water-deficit plants, with the latter showing reduced growth and physiological activity, confirming the water stress status of the plant. While the binary classification model successfully differentiates between well-watered and water-deficit plants, its practical use is limited. Therefore, a regression model was developed to directly predict predawn leaf water potential. To the best of our knowledge, this is the first time that electrical signals are correlated with vine water potential measurements. The findings presented here thus provide a promising new tool for future real-time and remote monitoring of vine water status to manage irrigation and adapt agronomic strategies. Nevertheless, validation and optimisation of the models are still necessary, particularly under field conditions.

Plant phenotyping relevance

ブドウの水分状態という植物生理形質を、植物電気生理信号と機械学習でリアルタイム推定する手法を開発・検証しており、表現型取得が研究の中心である。

Code and data availability

The supplied blocks describe grapevine electrophysiological data collection, ML classification/regression models, and SHAP analysis, but contain no public dataset, code, or model deposit with authors' availability language. All URLs in the blocks are citations to prior work or generic libraries (e.g., Optuna, SHAP), so

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