CNN-SVM hybrid model for varietal classification of wheat based on bulk samples

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dc.contributor.authorUnlersen, Muhammed Fahri
dc.contributor.authorSonmez, Mesut Ersin
dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorDemir, Bedrettin
dc.contributor.authorAydin, Nevzat
dc.contributor.authorSabanci, Kadir
dc.contributor.authorRopelewska, Ewa
dc.date.accessioned2024-02-23T13:43:32Z
dc.date.available2024-02-23T13:43:32Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractDetermining the variety of wheat is important to know the physical and chemical properties which may be useful in grain processing. It also affects the price of wheat in the food industry. In this study, a Convolutional Neural Network (CNN)-based model was proposed to determine wheat varieties. Images of four different piles of wheat, two of which were the bread and the remaining durum wheat, were taken and image pre-processing techniques were applied. Small-sized images were cropped from high-resolution images, followed by data augmentation. Then, deep features were extracted from the obtained images using pre-trained seven different CNN models (AlexNet, ResNet18, ResNet50, ResNet101, Inceptionv3, DenseNet201, and Inceptionresnetv2). Support Vector Machines (SVM) classifier was used to classify deep features. The classification accuracies obtained by classification with various kernel functions such as Linear, Quadratic, Cubic and Gaussian were compared. The highest wheat classification accuracy was achieved with the deep features extracted with the Densenet201 model. In the classification made with the Cubic kernel function of SVM, the accuracy value was 98.1%.en_US
dc.identifier.doi10.1007/s00217-022-04029-4
dc.identifier.endpage2052en_US
dc.identifier.issn1438-2377
dc.identifier.issn1438-2385
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85129831315en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2043en_US
dc.identifier.urihttps://doi.org/10.1007/s00217-022-04029-4
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10847
dc.identifier.volume248en_US
dc.identifier.wosWOS:000794076800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEuropean Food Research And Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectDeep Learningen_US
dc.subjectSupport Vector Machineen_US
dc.subjectWheat Classificationen_US
dc.titleCNN-SVM hybrid model for varietal classification of wheat based on bulk samplesen_US
dc.typeArticleen_US

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