Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response

TL;DR

This study demonstrates that statistical features extracted from plant electrical signals can effectively classify different external stimuli, potentially simplifying real-time monitoring of plant responses.

Contribution

The paper introduces a method using statistical features and discriminant analysis to classify stimuli based on plant electrical signals, including two standard features for improved efficiency.

Findings

Successful classification of stimuli using statistical features

Identification of two standard features for consistent results

Potential for simplified online stimulus classification

Abstract

Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli - Sodium Chloride (NaCl), Sulphuric Acid (H2SO4) and Ozone (O3). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.