Plant disease and pest detection using deep learning-based features

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:09:58Z
dc.date.available2024-08-04T20:09:58Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers.en_US
dc.identifier.doi10.3906/elk-1809-181
dc.identifier.endpage1651en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85065833813en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1636en_US
dc.identifier.trdizinid336832en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1809-181
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/336832
dc.identifier.urihttps://hdl.handle.net/11616/92534
dc.identifier.volume27en_US
dc.identifier.wosWOS:000469016000006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPlant disease and pest detectionen_US
dc.subjectconvolutional neural networksen_US
dc.subjectdeep learning architecturesen_US
dc.subjectfeature extractionen_US
dc.subjectclassifier methodsen_US
dc.titlePlant disease and pest detection using deep learning-based featuresen_US
dc.typeArticleen_US

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