Aquatic weed automatic classification using machine learning techniques

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dc.contributor.authorPereira, Luis A. M. [UNESP]
dc.contributor.authorNakamura, Rodrigo Y. M. [UNESP]
dc.contributor.authorde Souza, Guilherme F. S. [UNESP]
dc.contributor.authorMartins, Dagoberto [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:20:03Z
dc.date.available2014-05-20T13:20:03Z
dc.date.issued2012-09-01
dc.description.abstractAquatic weed control through chemical products has attracted much attention in the last years, mainly because of the ecological disorder caused by such plants, and also the consequences to the economical activities. However, this kind of control has been carried out in a non-automatic way by technicians, and may be a not healthy policy, since each species may react differently to the same herbicide. Thus, this work proposes the automatic identification of some species by means of supervised pattern recognition techniques and shape descriptors in order to compose a nearby future expert system for automatic application of the correct herbicide. Experiments using some state-of-the-art techniques have shown the robustness of the employed pattern recognition techniques. (c) 2012 Elsevier B.V. All rights reserved.en
dc.description.affiliationUNESP São Paulo State Univ, Dept Comp, Bauru, Brazil
dc.description.affiliationUNESP São Paulo State Univ, Dept Plant Prod, Botucatu, SP, Brazil
dc.description.affiliationUnespUNESP São Paulo State Univ, Dept Comp, Bauru, Brazil
dc.description.affiliationUnespUNESP São Paulo State Univ, Dept Plant Prod, Botucatu, SP, 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: 09/16206-1
dc.description.sponsorshipIdFAPESP: 10/12222-0
dc.description.sponsorshipIdFAPESP: 10/11676-7
dc.description.sponsorshipIdFAPESP: 11/14058-5
dc.description.sponsorshipIdFAPESP: 11/14094-1
dc.description.sponsorshipIdCNPq: 303182/2011-3
dc.format.extent56-63
dc.identifierhttp://dx.doi.org/10.1016/j.compag.2012.05.015
dc.identifier.citationComputers and Electronics In Agriculture. Oxford: Elsevier B.V., v. 87, p. 56-63, 2012.
dc.identifier.doi10.1016/j.compag.2012.05.015
dc.identifier.issn0168-1699
dc.identifier.lattes2340617938554636
dc.identifier.urihttp://hdl.handle.net/11449/5456
dc.identifier.wosWOS:000307681000007
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofComputers and Electronics in Agriculture
dc.relation.ispartofjcr2.427
dc.relation.ispartofsjr0,814
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectAquatic weeden
dc.subjectOptimum-path foresten
dc.subjectSupport vector machinesen
dc.subjectNaive Bayesen
dc.subjectArtificial neural networksen
dc.subjectShape analysisen
dc.titleAquatic weed automatic classification using machine learning techniquesen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes2340617938554636
unesp.author.orcid0000-0002-6494-7514[5]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências Agronômicas, Botucatupt
unesp.departmentProdução e Melhoramento Vegetal - FCApt

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Botucatu - FCA - Faculdade de Ciências Agronômicas