Bread and durum wheat classification using wavelet based image fusion

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dc.authorid0000-0003-0238-9606en_US
dc.authorid0000-0001-7549-0137en_US
dc.contributor.authorSabancı, Kadir
dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorDurdu, Akif
dc.date.accessioned2020-08-06T13:29:30Z
dc.date.available2020-08-06T13:29:30Z
dc.date.issued2020en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000551460000001 PubMed ID: 32608512en_US
dc.description.abstractBACKGROUND Wheat, which is an essential nutrient, is an important food source for human beings because it is used in flour and feed production. As in many nutrients, wheat plays an important role in macaroni and bread production. The types of wheat used for both foods are different, namely bread and durum wheat. A strong separation of these two wheat types is important for product quality. This article differs from the traditional methods available for the identification of bread and durum wheat species. In this study, ultraviolet (UV) and white light (WL) images of wheat are obtained for both species. Wheat types in these images are classified by various machine learning (ML) methods. Afterwards, these images are fused by wavelet-based image fusion method. RESULTS The highest accuracy value calculated using only UV and only WL image is 94.8276% and these accuracies are obtained by Support Vector Machine (SVM) and multilayer perceptron (MLP) algorithms, respectively. However, this accuracy value is 98.2759% for the fusion image and both MLP and SVM achieved the same success. CONCLUSION Wavelet-based fusion has increased the classification accuracy of all three learning algorithms. It is concluded that the identification ability in the resulting fusion image is higher than the other two raw images. (c) 2020 Society of Chemical Industryen_US
dc.identifier.citationSabanci, K., Aslan, M. F.,Durdu, A. (2020). Bread and durum wheat classification using wavelet based image fusion. Journal of the Science of Food and Agriculture.en_US
dc.identifier.doi10.1002/jsfa.10610
dc.identifier.issn0022-5142
dc.identifier.issn1097-0010
dc.identifier.pmid32608512
dc.identifier.scopus2-s2.0-85088393151
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/jsfa.10610
dc.identifier.urihttps://hdl.handle.net/11492/3624
dc.identifier.wosWOS:000551460000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSabancı, Kadir
dc.institutionauthorAslan, Muhammet Fatih
dc.language.isoen
dc.publisherWileyen_US
dc.relation.journalJournal of the Science of Food And Agricultureen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBread Wheaten_US
dc.subjectDurum Wheaten_US
dc.subjectMachine Learningen_US
dc.subjectWavelet-Based İmage Fusionen_US
dc.titleBread and durum wheat classification using wavelet based image fusionen_US
dc.typeArticle

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