Classification of hazelnut kernels with deep learning

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dc.authoridUNAL, Zeynep/0000-0002-9954-1151
dc.authoridAktas, Hakan/0000-0002-0188-7075
dc.contributor.authorUnal, Zeynep
dc.contributor.authorAktas, Hakan
dc.date.accessioned2024-11-07T13:25:04Z
dc.date.available2024-11-07T13:25:04Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study a deep learning-based, highly accurate network structure was applied to discriminate the whole kernel of hazelnut from other classes such as damaged kernel, shell, and undersized, which are present in small quantities after mechanized sorting and later subject to manual sorting. An industrial setup was developed to generate datasets for these four classes, and 2094 images of each class were recorded. With the obtained datasets, EfficientNetB0- EfficientNetB1- EfficientNetB2-EfficientNetB3 and InceptionV3 network structures were first trained from scratch and the highest test accuracy was calculated as 97.85 % for the InceptionV3 network structure. To further increase in the test accuracy, the transfer learning structure was used by applying the Imagenet coefficients. As a result of the training using Imagenet weights, the highest test accuracy was calculated as 99.28 % for the EfficientNetB2 and EfficientNetB3 structures. Although both networks give the same result in the classification of four hazelnut classes, the success in discrimination of the whole kernel of hazelnut from other classes is a key solution to decrease economic loss due to incorrect classification. From this point of view, according to confusion matrixes, it was concluded that the EfficientNetB3 structure is four times more efficient than the EfficientNetB2 structure.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Nig?de O?mer Halisdemir University; [MMT 2021/10-BAGEP]
dc.description.sponsorshipAcknowledgments This research has been supported by the Scientific Research Projects Coordination Unit of Nig?de O?mer Halisdemir University. Project Num-ber: MMT 2021/10-BAGEP, 2021.
dc.identifier.doi10.1016/j.postharvbio.2022.112225
dc.identifier.issn0925-5214
dc.identifier.issn1873-2356
dc.identifier.scopus2-s2.0-85144514896
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.postharvbio.2022.112225
dc.identifier.urihttps://hdl.handle.net/11480/14463
dc.identifier.volume197
dc.identifier.wosWOS:000909011900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofPostharvest Biology and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectDeep Learning
dc.subjectHazelnut Classification
dc.subjectCNN Architectures
dc.subjectImage Processing
dc.titleClassification of hazelnut kernels with deep learning
dc.typeArticle

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