  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.
