Abstract
Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches.
Highlights
Each year, approximately one-fourth of the agricultural production is damaged by herbivorous arthropods, such as spider mites [1,2]
The findings presented in [15] demonstrate continuous and stable long-term recordings of plant electrophysiology signals in regular greenhouse conditions with the newly developed sensor PhytlSigns (Vivent SA, Crans-prèsCeligny, Switzerland)
The aim of this study is to explore electrophysiology signals acquired with PhytlSigns from commercial tomato plants growing in a typical production environment during a spider mite infestation
Summary
Approximately one-fourth of the agricultural production is damaged by herbivorous arthropods, such as spider mites [1,2]. The findings presented in [15] demonstrate continuous and stable long-term recordings of plant electrophysiology signals in regular greenhouse conditions (i.e., outside of a Faraday cage) with the newly developed sensor PhytlSigns (Vivent SA, Crans-prèsCeligny, Switzerland) Such findings allow access to direct crop monitoring and potentially automated real-time assessment of a plant’s state in standard growing environments. The aim of this study is to explore electrophysiology signals acquired with PhytlSigns from commercial tomato plants growing in a typical production environment during a spider mite infestation It employs signal processing and supervised machine learning techniques to assess the possibility to automatically distinguish eventual pattern changes in the electrical plant response related to the plant stress induced by the presence of spider mites.
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