Characterizing Information Propagation in Plants

This paper considers an electro-chemical based communication model for intercellular communication in plants. Many plants, such as Mimosa pudica (the "sensitive plant"), employ electrochemical signals known as action potentials (APs) for communication purposes. In this paper we present a simple model for action potential generation. We make use of the concepts from molecular communication to explain the underlying process of information transfer in a plant. Using the information-theoretic analysis, we compute the mutual information between the input and output in this work. The key aim is to study the variations in the information propagation speed for varying number of plant cells for one simple case. Furthermore we study the impact of the AP signal on the mutual information and information propagation speed. We aim to explore further that how the growth rate in plants can impact the information transfer rate and vice versa.Comment: 6 pages, 5 Figures, Submitted to IEEE Conference, 201

Assessment of the universality of the electrophysiological signal acquired from tomatoes and eggplants

The electrical signaling system in plants represents the most efficient means for rapidly transmitting information about changes in the environment to all plant parts. Recent studies have shown that the application of machine learning techniques to the electrophysiological signal acquired on tomato plants growing under typical production conditions enables highly accurate detection of stress in plants due to either drought, nutrient deficiency, or pest attack. To better understand how specific are the acquired learnings to tomato plants only, this study aims to explore the extent of the universality of the electrophysiological signal from tomatoes and eggplants. To this end, we modeled the drought response in both tomato and eggplants individually, using recordings from 34 plants from each crop, and evaluated the performance of the classification models trained on data from one crop to the data from the other crop. Different features appear as the most discriminative for each crop. Therefore, several models were taken in this analysis, namely those trained with: i) all extracted features, ii) the most discriminative groups of features for the tomatoes, iii) the most discriminative groups of features for the eggplants, and iv) the union of the most discriminative groups of features for both crops. The obtained findings showed that the models built on data from one crop are able to predict the plant state of the other crop if they are trained with the set of features enclosing the most discriminative ones for the crop on which the model is being evaluated. Such findings imply some similarities in the electrophysiological signals acquired from these two crops with a certain level of crop specificity indicated by the dissimilarities between the discriminatory information for a specific stressor

Plant electrophysiology for smart irrigation management of greenhouse

Monitoring crop health is a daily routine for growers and farmers to manage and respond effectively and in a timely way to abiotic and biotic challenges, thus preventing crop loss and ensuring quality production. Digital technology allows remote sensing in real-time for precision agriculture. Many sensors are now deployed in the field to measure environmental factors such as weather conditions, soil conditions, insect populations, but sensors that directly target a plant’s physiological state are scarce. Recent advances in plant electrophysiology allow real-time measurement of electrical signals from plants in greenhouses under typical production conditions. Combined with machine learning techniques, electrophysiology can accurately predict physiological plant state modifications due to drought or nutrient deficiencies. Here, we have investigated the ability of an electrophysiology sensor to support real-time crop supervision and manage precision irrigation based on plant demand/needs. To address this aspect, an automated irrigation set-up has been developed and deployed in a real working environment, e.g., tomato soilless culture. Based on real-time monitoring of electrical signals, the irrigation system is turned on/off via a set of relay controllers according to a drought-prediction model applied in real-time via a single board computer, namely a Raspberry Pi. Different algorithms have been evaluated with a comparison between i) conventional greenhouse irrigation system vs. ii) electrophysiology-driven automated irrigation. We found that irrigation volumes provided to the crop by electrophysiology-driven system were similar to the control. A similar behaviour was also observed for the drainage. In addition, fruit quality parameters (°Brix, acidity, firmness) and yield were not affected. Measuring crop water status in real-time using plant electrophysiology would allow precision irrigation management and therefore improve resource management for sustainable agriculture

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