English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Plants monitor their surrounding environment and control their physiologicalfunctions by producing an electrical response. We recorded electrical signalsfrom different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3)and Sulfuric Acid (H2SO4) under laboratory conditions. After applyingpre-processing techniques such as filtering and drift removal, we extracted fewstatistical features from the acquired plant electrical signals. Using thesefeatures, combined with different classification algorithms, we used a decisiontree based multi-class classification strategy to identify the three differentexternal chemical stimuli. We here present our exploration to obtain theoptimum set of ranked feature and classifier combination that can separate aparticular chemical stimulus from the incoming stream of plant electricalsignals. The paper also reports an exhaustive comparison of similar featurebased classification using the filtered and the raw plant signals, containingthe high frequency stochastic part and also the low frequency trends present init, as two different cases for feature extraction. The work, presented in thispaper opens up new possibilities for using plant electrical signals to monitorand detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.

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