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

Abstract

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.

Printable Transparent Microelectrodes toward Mechanically and Visually Imperceptible Electronics

“…The electrophysiological signal of plants could be correlated with the water status, wounded parts, and photosynthetic activity, which would help producers with the healthcare of plants. [ 40–43 ] We used Egeria densa as the target plant because its leaves easily come in contact with a surrounding medium owing to their thin structure (1.9 μm thickness). [ 44 ] As the sensors, the cross‐aligned AgNW electrodes are patterned with a width of 60 μm.…”

“…Steady‐state data can provide important information for understanding the state of the plant because changes in the EP according to the plant state appear after a long time (>10 min) after the environment changes. [ 40,41,43,44 ] A previous study [ 41 ] also detected the EP difference and assessed the health of a plant in terms of its water stress conditions and nycthemeral rhythm. However, a complete understanding of the reason behind changes in the EPs of plants has not been realized because it requires the consideration of several factors such as the kinetics of ion transport and photosynthetic activity.…”

Electrome & Cognition Modes in Plants: A Transdisciplinary Approach to the Eco-Sensitiveness of the World

“…Several teams have recently automated the classification of EPG-type electrophysiological activities including variations in extracellular potential and the emission of spikes in real time following various stimulations or stresses by mathematical modeling or using machine learning (Pereira et al 2018;Tran et al, 2019) [13,14]. They reveal, thanks to the use of specific electrodes, the interest of the practice of monitoring in real time the physiological activity of plants without Faraday's cage, which could be applied with success using EPGs.…”

Early Diagnosis of Iron Deficiency in Commercial Tomato Crop Using Electrical Signals

“…Optimal crop management relies on regular monitoring to scout and detect problems before major crop stress occurs. We have previously shown that an electrophysiological sensor allows continuous and stable long-term monitoring of plant electrical signals for several weeks without affecting plant functions, which can be performed in a commercial greenhouse for crop production without a Faraday cage (Tran et al, 2019). We hypothesized that monitoring slow and long-term changes in electrical potential (EP) with the help of machine learning algorithms can be used as an agronomic tool to detect physiological plant state modifications.…”