Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

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.

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

“…Recent work [13] has demonstrated the possibility of acquiring continuous and stable long-term plant electrophysiology signals in typical greenhouse conditions. Further studies have shown that the signal recordings encode discriminative patterns for stress related to drought [13] and to the presence of spider mites [14].…”

“…A recent exploratory study on plant electrophysiology, introduces a methodology to classify the plant state related to the applied stimulus with an accuracy of 80% [14]. However, the related process of feature calculation i.e., extraction of the information from the recorded signal for transforming the raw signal data into a form on which the modelling of a classifier could be applied, is a time-consuming task which limits the potential use and benefits of this framework in daily agricultural practice.…”

“…This represents one of the major pest problems of tomato cultivated in greenhouse since the highly controlled environment of greenhouses favors low-humidity diseases. Plants were infested with spider mites (T. urticae) during two weeks as previously described [14], leading to a significant infestations.…”

“…Recordings of the electrophysiological signal of each plant were made with multichannel PhytlSigns devices, which use pairs of electrodes: an active one placed in a higher part of the plant stem and a reference (ground) electrode in the lower part [13,14]. The Recordings of the electrophysiological signal of each plant were made with multichannel PhytlSigns devices, which use pairs of electrodes: an active one placed in a higher part of the plant stem and a reference (ground) electrode in the lower part [13,14]. The placement of the active electrode and the device during acquisition is shown in Figure 1a,b, respectively.…”

Real-Time Remote Sensing of the Lobesia botrana Moth Using a Wireless Acoustic Detection Sensor

“…In order to make the detection autonomous, several studies have been developed in the last years. Different techniques such as image processing-based detection [15][16][17][18][19][20], capacitive sensors [21,22], radar techniques [23], optoelectronics [24], electrophysiological signals [25] and acoustic-and optoacoustic-based detection methods [26][27][28][29][30][31] have been proposed and tested in the field. Nevertheless, most of these technologies use post-processing in order to detect the pest and only a few of them use real-time processing of the information acquired and energy optimization for field conditions [21,22,32,33].…”