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dc.contributor.authorSabzi, Sajad
dc.contributor.authorPourdarbani, Razieh
dc.contributor.authorArribas Sánchez, Juan Ignacio 
dc.date.accessioned2022-03-24T09:09:31Z
dc.date.available2022-03-24T09:09:31Z
dc.date.issued2020
dc.identifier.citationComputers, 2020, vol. 9, n. 1, 6es
dc.identifier.issn2073-431Xes
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/52620
dc.descriptionProducción Científicaes
dc.description.abstractA computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.classificationComputer visiones
dc.subject.classificationVisión artificiales
dc.subject.classificationNeural networkses
dc.subject.classificationRedes neuronaleses
dc.subject.classificationPrecision agriculturees
dc.subject.classificationAgricultura de precisiónes
dc.titleA computer vision system for the automatic classification of five varieties of tree leaf imageses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2020 The Authorses
dc.identifier.doi10.3390/computers9010006es
dc.relation.publisherversionhttps://www.mdpi.com/2073-431X/9/1/6es
dc.peerreviewedSIes
dc.description.projectUnión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP)es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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