Classification of plant electrophysiology signals for detection of spider mites infestation in tomatoes

Najdenovska, Elena (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Dutoit, Fabien (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Tran, Daniel (Agroscope, Conthey, Switzerland) ; Plummer, Carole (Vivent SA, Gland, Switzerland) ; Wallbridge, Nigel (Vivent SA, Gland, Switzerland) ; Camps, Cédric (Agroscope, Conthey, Switzerland) ; Raileanu, Laura Elena (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

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.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Date:
2021-02
Pagination:
16 p.
Published in:
Applied Sciences
Numeration (vol. no.):
2021, vol. 11, no. 4, article no. 1414
DOI:
ISSN:
2076-3417
Appears in Collection:



 Record created 2021-02-23, last modified 2021-02-25

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