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Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response

Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response
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 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.
0925-4005
278-295
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
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Colzi, Ilaria
ddccb518-6950-4fc1-badb-2e31e23b300d
Mancuso, Stefano
e9925eea-3fd7-418f-8f30-783f395000a1
Vitaletti, Andrea
c3fd5ffa-d2eb-4199-9e28-83b5a563e324
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
Colzi, Ilaria
ddccb518-6950-4fc1-badb-2e31e23b300d
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, Colzi, Ilaria, Mancuso, Stefano and Vitaletti, Andrea (2017) Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response. Sensors and Actuators B: Chemical, 249, 278-295. (doi:10.1016/j.snb.2017.04.071).

Record type: Article

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.

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Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response - Accepted Manuscript
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Accepted/In Press date: 12 April 2017
e-pub ahead of print date: 14 April 2017
Published date: October 2017
Organisations: Electronics & Computer Science, Electronic & Software Systems

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Local EPrints ID: 412035
URI: https://eprints.soton.ac.uk/id/eprint/412035
ISSN: 0925-4005
PURE UUID: c5ddf561-ce1b-486a-baa0-fe9c3c0d3d7f

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Date deposited: 05 Jul 2017 16:31
Last modified: 24 Sep 2019 04:51

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