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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/10216/56784
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dc.contributor.authorBernardes, A. A.-
dc.contributor.authorRogeri, J. G.-
dc.contributor.authorMarranghello, N.-
dc.contributor.authorPereira, A. S.-
dc.contributor.authorAraujo, A. F.-
dc.contributor.authorTavares, João Manuel R. S.-
dc.date.accessioned2014-05-27T11:26:23Z-
dc.date.accessioned2016-10-25T18:36:36Z-
dc.date.available2014-05-27T11:26:23Z-
dc.date.available2016-10-25T18:36:36Z-
dc.date.issued2012-02-13-
dc.identifierhttp://dx.doi.org/10.1007/978-94-007-0726-9_4-
dc.identifierhttp://hdl.handle.net/10216/56784-
dc.identifier.citationComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 193-197.-
dc.identifier.urihttp://hdl.handle.net/11449/73186-
dc.identifier.urihttp://acervodigital.unesp.br/handle/10216/56784-
dc.description.abstractThe pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.en
dc.format.extent193-197-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectAscochyta blight-
dc.subjectAutomatic classification-
dc.subjectBacterial blight-
dc.subjectCrop quality-
dc.subjectDigital image-
dc.subjectFoliar disease-
dc.subjectCotton-
dc.subjectDamage detection-
dc.subjectFeature extraction-
dc.subjectImage processing-
dc.subjectMedical image processing-
dc.subjectPlants (botany)-
dc.subjectCrops-
dc.titleIdentification of foliar diseases in cotton cropen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionInstitute of Mechanical Engineering and Industrial Management (INEGI)-
dc.description.affiliationDepartment of Computer Science and Statistics IBILCE Sao Paulo State University ( UNESP), Sao Jose do Rio Preto-SP-
dc.description.affiliationDepartment of Mechanical Engineering (DeMec) University of Porto (FEUP) Institute of Mechanical Engineering and Industrial Management (INEGI), Porto-
dc.description.affiliationUnespDepartment of Computer Science and Statistics IBILCE Sao Paulo State University ( UNESP), Sao Jose do Rio Preto-SP-
dc.identifier.doi10.1007/978-94-007-0726-9_4-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing-
dc.identifier.scopus2-s2.0-84856703865-
dc.identifier.orcid0000-0003-1086-3312pt
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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