Recognition of sun-pest infected wheat kernels using Artificial Neural Networks

dc.contributor.authorBabalak, Lahmet
dc.contributor.authorBaykan, Oemer Kaan
dc.contributor.authorBotsah, Fatih M.
dc.date.accessioned2020-03-26T17:18:02Z
dc.date.available2020-03-26T17:18:02Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.descriptionIEEE 15th Signal Processing and Communications Applications Conference -- JUN 11-13, 2007 -- Eskisehir, TURKEYen_US
dc.description.abstractIn this study it is aimed to recognize sun-pest infected kernels in a sample sub-group of wheat kernels taken from a bulk of Bezostaja wheat. Recognition of the damaged kernels is realized by evaluating light transmittance data of the kernels through use of Artificial Neural Networks (ANN). Wheat kernels in the sub-group are left to fall in an oblique groove with semi-circular cross-section. While the kernels cross a LED light source, light transmitted through the kernel fall on a sensor just across the light source. Analog signals induced by the sensor are recorded and histograms of these signals are evaluated by using ANN in order to recognize sun-pest infected wheat kernels in the sub-group. Two different ANN models: Multi Layer Perceptron (MLP) and Self Organizing Map (SOM) models were used in the recognition process.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-1-4244-0719-4
dc.identifier.startpage306en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21567
dc.identifier.wosWOS:000252924600077en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2007 IEEE 15TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1-3en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleRecognition of sun-pest infected wheat kernels using Artificial Neural Networksen_US
dc.typeConference Objecten_US

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