Grain classifier with computer vision using adaptive neuro-fuzzy inference system

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dc.authorid0000-0003-0238-9606en_US
dc.authorid0000-0002-7687-9061en_US
dc.authorid0000-0002-9756-8756en_US
dc.contributor.authorSabancı, Kadir
dc.contributor.authorToktaş, Abdurrahim
dc.contributor.authorKayabaşı, Ahmet
dc.date.accessioned2019-12-06T21:15:53Z
dc.date.available2019-12-06T21:15:53Z
dc.date.issued2017
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000407498100012en_US
dc.descriptionPubMed:28194800en_US
dc.description.abstractBACKGROUNDA computer vision-based classifier using an adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 for durum) are taken by a high-resolution camera. Visual feature data of the grains related to dimension (#4), color (#3) and texture (#5) as inputs of the classifier are mainly acquired for each grain using image processing techniques (IPTs). In addition to these main data, nine features are reproduced from the main features to ensure a varied population. Thus four sub-sets including categorized features of reproduced data are constituted to examine their effects on the classification. In order to simplify the classifier, the most effective visual features on the results are investigated. RESULTSThe data sets are compared with each other regarding classification accuracy. A simplified classifier having seven selected features is achieved with the best results. In the testing process, the simplified classifier computes the output with 99.46% accuracy and assorts the wheat grains with 100% accuracy. CONCLUSIONA system which classifies wheat grains with higher accuracy is designed. The proposed classifier integrated to industrial applications can automatically classify a variety of wheat grains. (c) 2017 Society of Chemical Industryen_US
dc.identifier.citationSabancı, K., Toktaş, A., Kayabaşı, A. (2017). Grain classifier with computer vision using adaptive neuro-fuzzy inference system. Journal of the Science of Food and Agriculture, 97, 12, 3994-4000.
dc.identifier.doi10.1002/jsfa.8264
dc.identifier.endpage4000en_US
dc.identifier.issn0022-5142
dc.identifier.issn1097-0010
dc.identifier.issue12en_US
dc.identifier.pmid28194800
dc.identifier.scopus2-s2.0-85014508505
dc.identifier.scopusqualityQ1
dc.identifier.startpage3994en_US
dc.identifier.urihttps://dx.doi.org/10.1002/jsfa.8264
dc.identifier.urihttps://hdl.handle.net/11492/2752
dc.identifier.volume97en_US
dc.identifier.wosWOS:000407498100012
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSabancı, Kadir
dc.institutionauthorToktaş, Abdurrahim
dc.institutionauthorKayabaşı, Ahmet
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.subjectWheat Grainsen_US
dc.subjectClassificationen_US
dc.subjectImage Processingen_US
dc.subjectFeature Selectionen_US
dc.subjectAdaptive Neuro-Fuzzy Inference System (ANFIS)en_US
dc.titleGrain classifier with computer vision using adaptive neuro-fuzzy inference systemen_US
dc.typeArticle

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