Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response
This work addresses the challenge of interpreting plant electrical responses for environmental sensing, offering a method to reduce computational complexity for online classification, though it is incremental in applying existing statistical and classification methods to this domain.
The paper tackled the problem of classifying external stimuli from plant electrical signals by computing 11 statistical features from raw time series data, achieving successful classification using discriminant analysis techniques and identifying two standard features that consistently performed well for three stimuli types.
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.