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.

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

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.

Surrounded: A Series of Sound Installations That Combine Plant Electrophysiology and 3D Sonic Art

This paper discusses a series of sound installations that combine plant electrophysiology with 3D sonic art. A brief introduction to plant electrophysiology is given. The sonification of electrophysiological signals in the mycorrhizal network is discussed, explaining how art and science are combined in this project in a way that differs from the simple sonification of data. Novel 3D audio spatialization techniques, the 3D audio mapping of natural environments and immersion are also discussed, along with technical details of how to read the electrical signals in plants known as action potentials. Other topics addressed include acoustic signaling in the forest, spectral composition and interaction with forest flora and fauna.