A novel approach to water stress assessment in plants: New bioimpedance method with PSO-optimized Cole-Cole impedance modeling

Abstract

In order to characterize plant water deficiencies, this paper presents a custom-developed bioimpedance (BIS) measurement setup designed for in vivo studies that extracts plant leaf parameters using a novel optimization approach based on the particle swarm optimization (PSO) algorithm. The system performs, four-electrode measurements on plant leaves and employs a custom multi-objective cost function to validate parameters for the Double Shell Cole-Cole model. The experiment consisted of two parts: first, pepper plants (Capsicum annuum L.) as a model plant were exposed to drought stress in a light chamber, and their impedance and physiological parameters were measured. In the second part of the experiment, detached pepper leaves were allowed to dry naturally, and impedance measurements were recorded at hourly and tri-hourly intervals. Impedance spectrum measurements from 230 samples (1 Hz to 100 kHz), collected during both experiments, demonstrated that extracellular fluid resistance increases linearly with water loss. The proposed PSO-optimized Double Shell model showed a stronger correlation between extracellular fluid resistance and water loss compared to the widely used Zfit algorithm, which exhibited higher coefficient of variation in the Cole-Cole parameters. Both algorithms showed a significant negative correlation between relative water content and extracellular fluid resistance, but only the proposed PSO-based model detected a relationship between cell membrane capacity and membrane stability index. Additionally, extracellular fluid resistance correlated with photosynthetic efficiency. The results highlight the effectiveness of impedance measurements for assessing plant water status and support the reliability of proposed PSO-based optimization for bioimpedance analysis.

Plant phenotyping relevance

植物の水分状態を測定するバイオインピーダンス計測系とPSO最適化モデルを開発・比較検証しており、植物フェノタイプ取得手法が研究の中心である。

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