Title: Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response,
Abstract: Plants monitor their surrounding environment and control their physiological
functions by producing an electrical response. We recorded electrical signals
from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3)
and Sulfuric Acid (H2SO4) under laboratory conditions. After applying
pre-processing techniques such as filtering and drift removal, we extracted few
statistical features from the acquired plant electrical signals. Using these
features, combined with different classification algorithms, we used a decision
tree based multi-class classification strategy to identify the three different
external chemical stimuli. We here present our exploration to obtain the
optimum set of ranked feature and classifier combination that can separate a
particular chemical stimulus from the incoming stream of plant electrical
signals. The paper also reports an exhaustive comparison of similar feature
based classification using the filtered and the raw plant signals, containing
the high frequency stochastic part and also the low frequency trends present in
it, as two different cases for feature extraction. The work, presented in this
paper opens up new possibilities for using plant electrical signals to monitor
and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.