Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic

dc.contributor.authorPereira, Danillo Roberto
dc.contributor.authorPapa, Joao Paulo [UNESP]
dc.contributor.authorRosalin Saraiva, Gustavo Francisco
dc.contributor.authorSouza, Gustavo Maia
dc.contributor.institutionUniv Oeste Paulista
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Fed Pelotas
dc.date.accessioned2018-11-29T08:01:21Z
dc.date.available2018-11-29T08:01:21Z
dc.date.issued2018-02-01
dc.description.abstractIn 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.en
dc.description.affiliationUniv Oeste Paulista, Sao Paulo, Brazil
dc.description.affiliationUniv Estadual Paulista, Sao Paulo, Brazil
dc.description.affiliationUniv Fed Pelotas, Pelotas, RS, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Sao Paulo, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/16250-9
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.format.extent35-42
dc.identifierhttp://dx.doi.org/10.1016/j.compag.2017.12.024
dc.identifier.citationComputers And Electronics In Agriculture. Oxford: Elsevier Sci Ltd, v. 145, p. 35-42, 2018.
dc.identifier.doi10.1016/j.compag.2017.12.024
dc.identifier.fileWOS000425577400005.pdf
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/11449/166017
dc.identifier.wosWOS:000425577400005
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofComputers And Electronics In Agriculture
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectPlant stress
dc.subjectOptimum-Path Forest
dc.subjectConvolutional Neural Networks
dc.subjectInterval Arithmetic
dc.titleAutomatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmeticen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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