Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features

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dc.contributor.authorKaya, Esra.
dc.contributor.authorSaritas, Ismail.
dc.date.accessioned2020-03-26T20:19:38Z
dc.date.available2020-03-26T20:19:38Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractWheat is the main ingredient of most common food products in our daily lives and obtaining good quality wheat kernels is an important matter for the production of food supplies. In this study, type-1252 durum wheat kernels which have vast harvest areas in Turkey and is the principal ingredient of pasta and semolina products were examined and classified to obtain top quality wheat kernels based on their vitreousness. Also, top quality provision of food supplies means that the products must be refined from all foreign materials so a classification process has been applied to extract foreign materials from wheat kernels. In this study, we have used a total of 236 morphological, colour, wavelet and gaborlet features to classify vitreous, starchy durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amount of features based on the feature rank list obtained with ANOVA test. The data we have used in this study was video images of wheat kernels and foreign objects present on a conveyor belt camera system with illumination provided by daylight colour powerleds. The maximum classification accuracy was 93.46% obtained with 210 feature neural network function which was generated and applied on the video containing a mixture of wheat kernels and foreign objects.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects Unit (BAP)Selcuk University; Selcuk University Instructor Training Program Unit (OYP)en_US
dc.description.sponsorshipAppreciations to Selcuk University Scientific Research Projects Unit (BAP) and Selcuk University Instructor Training Program Unit (OYP) for their financial support of this project.en_US
dc.identifier.citationKaya, E., Saritas, İ. (2019). Towards a Real-Time Sorting System: Identification of Vitreous Durum Wheat Kernels Using ANN Based on Their Morphological, Colour, Wavelet and Gaborlet Features. Computers and Electronics in Agriculture, 166, 1-9.
dc.identifier.doi10.1016/j.compag.2019.105016en_US
dc.identifier.issn0168-1699en_US
dc.identifier.issn1872-7107en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compag.2019.105016
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38346
dc.identifier.volume166en_US
dc.identifier.wosWOS:000497247500029en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKaya, Esra.
dc.institutionauthorSaritas, Ismail.
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTDen_US
dc.relation.ispartofCOMPUTERS AND ELECTRONICS IN AGRICULTUREen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectANNen_US
dc.subjectDurum wheaten_US
dc.subjectGaborleten_US
dc.subjectVitreousnessen_US
dc.subjectWaveleten_US
dc.titleTowards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet featuresen_US
dc.typeArticleen_US

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