U2-Net Segmentation And Multi-Label Cnn Classification Of Wheat Varieties

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dc.authoridTürk, Fuat/0000-0001-8159-360X
dc.authoridCivelek, Zafer/0000-0001-6838-3149
dc.authoridArgun, Mustafa Şamil/0000-0001-8209-3164
dc.contributor.authorArgun, Mustafa Şamil
dc.contributor.authorTürk, Fuat
dc.contributor.authorCivelek, Zafer
dc.date.accessioned2025-01-21T16:33:55Z
dc.date.available2025-01-21T16:33:55Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThere are many varieties of wheat grown around the world. In addition, they have different physiological states such as vitreous and yellow berry. These reasons make it difficult to classify wheat by experts. In this study, a workflow was carried out for both segmentation of wheat according to its vitreous/yellow berry grain status and classification according to variety. Unlike previous studies, automatic segmentation of wheat images was carried out with the U2-NET architecture. Thus, roughness and shadows on the image are minimized. This increased the level of success in classification. The newly proposed CNN architecture is run in two stages. In the first stage, wheat was sorted as vitreous-yellow berry. In the second stage, these separated wheats were grouped by multi-label classification. Experimental results showed that the accuracy for binary classification was 98.71% and the multi-label classification average accuracy was 89.5%. The results showed that the proposed study has the potential to contribute to making the wheat classification process more reliable, effective, and objective by helping the experts.
dc.identifier.doi10.36306/konjes.1364509
dc.identifier.issn2667-8055
dc.identifier.issue2
dc.identifier.trdizinid1242910
dc.identifier.urihttps://doi.org/10.36306/konjes.1364509
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay1242910
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23881
dc.identifier.volume12
dc.identifier.wosWOS:001312977700006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherKonya Teknik Univ
dc.relation.ispartofKonya Journal of Engineering Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectWheat Segmentation with U2-NET; U2-NET Architecture; Multi-Label CNN Classification; Wheat Classification
dc.titleU2-Net Segmentation And Multi-Label Cnn Classification Of Wheat Varieties
dc.typeArticle

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