Electrophysiological assessment of plant status outside a Faraday cage using supervised machine learning

Tran, Daniel (Institute for Plant Production Sciences, Agroscope, Conthey, Switzerland) ; Dutoit, Fabien (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Najdenovska, Elena (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Wallbridge, Nigel (Vivent SÁRL, Crans-près-Céligny, Switzerland) ; Plummer, Carrol (Vivent SÁRL, Crans-près-Céligny, Switzerland) ; Mazza, Marco (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Raileanu, Laura Elena (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Camps, Cédric (Institute for Plant Production Sciences, Agroscope, Conthey, Switzerland)

Living organisms have evolved complex signaling networks to drive appropriate physiological processes in response to changing environmental conditions. Amongst them, electric signals are a universal method to rapidly transmit information. In animals, bioelectrical activity measurements in the heart or the brain provide information about health status. In plants, practical measurements of bioelectrical activity are in their infancy and transposition of technology used in human medicine could therefore, by analogy provide insight about the physiological status of plants. This paper reports on the development and testing of an innovative electrophysiological sensor that can be used in greenhouse production conditions, without a Faraday cage, enabling real-time electric signal measurements. The bioelectrical activity is modified in response to water stress conditions or to nycthemeral rhythm. Furthermore, the automatic classification of plant status using supervised machine learning allows detection of these physiological modifications. This sensor represents an efficient alternative agronomic tool at the service of producers for decision support or for taking preventive measures before initial visual symptoms of plant stress appear.


Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
IPRINT - Institut de printing
Date:
2019-11
Pagination:
9 p.
Published in:
Scientific Reports
Numeration (vol. no.):
2019, vol. 9, art. no. 17073
DOI:
ISSN:
2045-2322
Appears in Collection:



 Record created 2019-11-26, last modified 2019-12-03

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