title: Exploring strategies for classification of external stimuli using statistical features of the plant electrical response.
abstract: 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), sulfuric 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.
authors: School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
authors: Shre Kumar
authors: SK
authors: Chatterjee
authors: School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK sd2a11@ecs.soton.ac.uk s.das@soton.ac.uk.
authors: Saptarshi
authors: S
authors: Das
authors: School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
authors: Koushik
authors: K
authors: Maharatna
authors: Department of Agri-food Production and Environmental Science (DISPAA), University of Florence, viale delle Idee 30, Sesto Fiorentino, Florence 50019, Italy.
authors: Elisa
authors: E
authors: Masi
authors: Department of Agri-food Production and Environmental Science (DISPAA), University of Florence, viale delle Idee 30, Sesto Fiorentino, Florence 50019, Italy.
authors: Luisa
authors: L
authors: Santopolo
authors: Department of Agri-food Production and Environmental Science (DISPAA), University of Florence, viale delle Idee 30, Sesto Fiorentino, Florence 50019, Italy.
authors: Stefano
authors: S
authors: Mancuso
authors: WLAB S.r.L., via Adolfo Ravà 124, Rome 00142, Italy DIAG, SAPIENZA Università di Roma, via Ariosto 25, Rome 00185, Italy.
authors: Andrea
authors: A
authors: Vitaletti
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016002:Discriminant Analysis; D004560:Electricity; D018551:Solanum lycopersicum / Q000502:physiology; D015233:Models, Statistical; D010126:Ozone; D018521:Plant Physiological Phenomena; D010944:Plants / Q000378:metabolism*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012965:Sodium Chloride / Q000737:chemistry; D012989:Soil Pollutants / Q000737:chemistry; D013269:Stochastic Processes; D013464:Sulfuric Acids / Q000737:chemistry
chemical_list: D012989:Soil Pollutants; D013464:Sulfuric Acids; D012965:Sodium Chloride; D010126:Ozone; C033158:sulfuric acid
keywords: classification; discriminant analysis; plant electrical signal; statistical feature; time-series analysis
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