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Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response
Exploring strategies for classification of external stimuli using statistical features of the plant electrical response
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
plant electrical signal, classification, discriminant analysis, statistical feature, time-series analysis
1742-5689
20141225
Chatterjee, Shre Kumar
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Das, Saptarshi
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Maharatna, Koushik
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Masi, Elisa
f63b0e64-1daf-4c10-a8a5-01823b505d51
Santopolo, Luisa
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Mancuso, Stefano
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Vitaletti, Andrea
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Chatterjee, Shre Kumar
aaa84ab8-3968-42b1-a9e1-d2a2e03c7b0a
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Masi, Elisa
f63b0e64-1daf-4c10-a8a5-01823b505d51
Santopolo, Luisa
7ed5bc8a-4166-40c7-9d44-de9991e3fe7a
Mancuso, Stefano
e9925eea-3fd7-418f-8f30-783f395000a1
Vitaletti, Andrea
c3fd5ffa-d2eb-4199-9e28-83b5a563e324

Chatterjee, Shre Kumar, Das, Saptarshi, Maharatna, Koushik, Masi, Elisa, Santopolo, Luisa, Mancuso, Stefano and Vitaletti, Andrea (2015) Exploring strategies for classification of external stimuli using statistical features of the plant electrical response. Journal of the Royal Society, Interface, 12 (104), 20141225. (doi:10.1098/rsif.2014.1225). (PMID:25631569)

Record type: Article

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

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Accepted/In Press date: 2 January 2015
e-pub ahead of print date: 28 January 2015
Published date: 6 March 2015
Keywords: plant electrical signal, classification, discriminant analysis, statistical feature, time-series analysis
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 373788
URI: https://eprints.soton.ac.uk/id/eprint/373788
ISSN: 1742-5689
PURE UUID: c25b49d0-5b44-4c5f-a170-d7424f081c41

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Date deposited: 30 Jan 2015 15:31
Last modified: 02 Nov 2017 17:32

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