Detection of Apple Leaf Diseases using Faster R-CNN

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dc.contributor.authorTuncer, Adem
dc.contributor.authorÖzen, Yunus
dc.contributor.authorSardogan, Melike
dc.date.accessioned2023-04-10T20:23:53Z
dc.date.available2023-04-10T20:23:53Z
dc.date.issued2020
dc.departmentRektörlük, Rektörlüğe Bağlı Birimler, Düzce Üniversitesi Dergilerien_US
dc.description.abstractImage recognition-based automated disease detection systems play an important role in the early detection of plantleaf diseases. In this study, an apple leaf disease detection system was proposed using Faster Region-BasedConvolutional Neural Network (Faster R-CNN) with Inception v2 architecture. Applications for the detection ofdiseases were carried out in apple orchards in Yalova, Turkey. Leaf images were obtained from different appleorchards for two years. In our observations, it was determined that apple trees of Yalova had black spot (venturiainaequalis) disease. The proposed system in the study detects a large number of leaves in an image, thensuccessfully classifies diseased and healthy ones. The disease detection system trained has achieved an average of84.5% accuracy.en_US
dc.identifier.doi10.29130/dubited.648387
dc.identifier.endpage1117en_US
dc.identifier.issn2148-2446
dc.identifier.issue1en_US
dc.identifier.startpage1110en_US
dc.identifier.trdizinid390326en_US
dc.identifier.urihttp://doi.org/10.29130/dubited.648387
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/390326
dc.identifier.urihttps://hdl.handle.net/20.500.12684/11612
dc.identifier.volume8en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofDüzce Üniversitesi Bilim ve Teknoloji Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleDetection of Apple Leaf Diseases using Faster R-CNNen_US
dc.typeArticleen_US

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