Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response

Plants monitor their surrounding environment and control their physiological functions by producingan electrical response. We recorded electrical signals from different plants by exposing them to SodiumChloride (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 theacquired 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 externalchemical stimuli. We here present our exploration to obtain the optimum set of ranked feature andclassifier combination that can separate a particular chemical stimulus from the incoming stream of plantelectrical signals. The paper also reports an exhaustive comparison of similar feature based classificationusing the filtered and the raw plant signals, containing the high frequency stochastic part and also thelow frequency trends present in it, as two different cases for feature extraction. The work, presentedin this paper opens up new possibilities for using plant electrical signals to monitor and detect otherenvironmental stimuli apart from NaCl, O3and H2SO4in future.