Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology

Automated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there have been no studies relating electrical plant response to general stress responses as a proxy for plant health. This study models general stress of plants exposed to either biotic or abiotic stressors, namely drought, nutrient deficiencies or infestation with spider mites, using electrophysiological signals acquired from 36 plants. Moreover, in the signal processing procedure, the proposed workflow reuses information from the previous steps, therefore considerably reducing computation time regarding recent related approaches in the literature. Careful choice of the principal parameters leads to a classification of the general stress in plants with more than 80% accuracy. The main descriptive statistics measured together with the Hjorth complexity provide the most discriminative information for such classification. The presented findings open new paths to explore for improved monitoring of plant health.

Automated summary

Plant health monitoring is becoming increasingly important in optimizing agricultural production, and studies have shown that plant electrophysiology can be used as a tool for determining plant status under stress. This study models general stress in plants exposed to various stressors using electrophysiological signals, achieving more than 80% accuracy in classification. Descriptive statistics and Hjorth complexity are key in providing discriminative information for classification.