Abstract
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.
Highlights
Automated monitoring of plant development is becoming a key enabler of for optimized agricultural production [1,2]
Early diagnosis would lead to reduced application of agrochemicals and an increased use of environmentally friendly practices, such as biological control agents
In other words, automated plant health monitoring could lead to significantly improved yields and efficient and environmentally sustainable crop protection
Summary
Automated monitoring of plant development is becoming a key enabler of for optimized agricultural production [1,2]. Given the limitations of the current literature, the goal of the presented study is twofold It introduces a novel approach for identifying a stressed state, in a general sense, in tomato plants growing in a typical production environment by combining information from their electrophysiological response to different stimuli, namely drought, nutrient deficiency and infestation with spider mites. It aims to identify the most discriminative features allowing the identification of the source of plant stress. By using information from previous steps in the signal processing procedure, the proposed approach tends to decrease the required computation time compared with recent approaches in related state of the art
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