Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.compag.2017.12.024 http://hdl.handle.net/11449/166017 |
Resumo: | In plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area. |
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Repositório Institucional da UNESP |
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Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmeticPlant stressOptimum-Path ForestConvolutional Neural NetworksInterval ArithmeticIn plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Oeste Paulista, Sao Paulo, BrazilUniv Estadual Paulista, Sao Paulo, BrazilUniv Fed Pelotas, Pelotas, RS, BrazilUniv Estadual Paulista, Sao Paulo, BrazilFAPESP: 2013/07375-0FAPESP: 2014/16250-9FAPESP: 2014/12236-1FAPESP: 2016/19403-6CNPq: 306166/2014-3Elsevier B.V.Univ Oeste PaulistaUniversidade Estadual Paulista (Unesp)Univ Fed PelotasPereira, Danillo RobertoPapa, Joao Paulo [UNESP]Rosalin Saraiva, Gustavo FranciscoSouza, Gustavo Maia2018-11-29T08:01:21Z2018-11-29T08:01:21Z2018-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article35-42application/pdfhttp://dx.doi.org/10.1016/j.compag.2017.12.024Computers And Electronics In Agriculture. Oxford: Elsevier Sci Ltd, v. 145, p. 35-42, 2018.0168-1699http://hdl.handle.net/11449/16601710.1016/j.compag.2017.12.024WOS:000425577400005WOS000425577400005.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers And Electronics In Agricultureinfo:eu-repo/semantics/openAccess2024-04-23T16:10:42Zoai:repositorio.unesp.br:11449/166017Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:10:42Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
title |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
spellingShingle |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic Pereira, Danillo Roberto Plant stress Optimum-Path Forest Convolutional Neural Networks Interval Arithmetic |
title_short |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
title_full |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
title_fullStr |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
title_full_unstemmed |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
title_sort |
Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic |
author |
Pereira, Danillo Roberto |
author_facet |
Pereira, Danillo Roberto Papa, Joao Paulo [UNESP] Rosalin Saraiva, Gustavo Francisco Souza, Gustavo Maia |
author_role |
author |
author2 |
Papa, Joao Paulo [UNESP] Rosalin Saraiva, Gustavo Francisco Souza, Gustavo Maia |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Univ Oeste Paulista Universidade Estadual Paulista (Unesp) Univ Fed Pelotas |
dc.contributor.author.fl_str_mv |
Pereira, Danillo Roberto Papa, Joao Paulo [UNESP] Rosalin Saraiva, Gustavo Francisco Souza, Gustavo Maia |
dc.subject.por.fl_str_mv |
Plant stress Optimum-Path Forest Convolutional Neural Networks Interval Arithmetic |
topic |
Plant stress Optimum-Path Forest Convolutional Neural Networks Interval Arithmetic |
description |
In plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-29T08:01:21Z 2018-11-29T08:01:21Z 2018-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.compag.2017.12.024 Computers And Electronics In Agriculture. Oxford: Elsevier Sci Ltd, v. 145, p. 35-42, 2018. 0168-1699 http://hdl.handle.net/11449/166017 10.1016/j.compag.2017.12.024 WOS:000425577400005 WOS000425577400005.pdf |
url |
http://dx.doi.org/10.1016/j.compag.2017.12.024 http://hdl.handle.net/11449/166017 |
identifier_str_mv |
Computers And Electronics In Agriculture. Oxford: Elsevier Sci Ltd, v. 145, p. 35-42, 2018. 0168-1699 10.1016/j.compag.2017.12.024 WOS:000425577400005 WOS000425577400005.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers And Electronics In Agriculture |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
35-42 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964488349253632 |