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Plants sense their environment by producing electrical signals which inessence represent changes in underlying physiological processes. Theseelectrical signals, when monitored, show both stochastic and deterministicdynamics. In this paper, we compute 11 statistical features from the rawnon-stationary plant electrical signal time series to classify the stimulusapplied (causing the electrical signal). By using different discriminantanalysis based classification techniques, we successfully establish that thereis enough information in the raw electrical signal to classify the stimuli. Inthe process, we also propose two standard features which consistently give goodclassification results for three types of stimuli - Sodium Chloride (NaCl),Sulphuric Acid (H2SO4) and Ozone (O3). This may facilitate reduction in thecomplexity involved in computing all the features for online classification ofsimilar external stimuli in future.

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