An application for the classification of egg quality and haugh unit based on characteristic egg features using machine learning models

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dc.contributor.authorSehirli, Eftal
dc.contributor.authorArslan, Kubra
dc.date.accessioned2024-09-29T15:57:10Z
dc.date.available2024-09-29T15:57:10Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractWith the increase in the world population, the nutritional needs of people have been increased. The demand for eggs which is one of the most important food sources has been increased over years. Therefore, it is very important to inform people about egg quality in order to be prepared for adverse situations such as substitution, mislabeling, and fraud. In this study, it is aimed to specify egg quality without using haugh unit (HU). Besides, another aim is to find how much information HU carries about the specification of egg quality. A dataset including 20 features related to eggs taken from 438 chickens created by Poultry Research Institute (PRI) has been analyzed. An application that can classify egg qualities as very good and excellent using machine learning (ML) models like decision tree (DT), linear discriminant analysis (LDA), logistic regression (LR), naive bayes (NB), support vector machines (SVM), K-nearest neighboring (KNN), random forest (RF) and artificial neural networks (ANN) has been developed in this study. In addition to that, HU at the 24th week and the 32nd week which are the most important classifier to determine egg qualities have been classified as low informative, medium informative, and high informative by the developed application. Egg quality has the best been classified by LR model based on accuracy and Matthews correlation coefficient (MCC) values as 98.6% and 0.96, respectively. HU at the 24th week has the best been classified by RF based on accuracy and MCC as 96.8% and 0.93, respectively. HU at the 32nd week has the best been classified by RF based on accuracy and MCC as 95.1% and 0.92, respectively. This paper mainly focuses on the classification of egg quality based on not only HU but also egg characteristic features and the importance of the informative feature of HU to classify egg quality.en_US
dc.description.sponsorshipTUBITAK 1003 project [118O056, 118O059]en_US
dc.description.sponsorshipThis article is written in scope of Kubra ARSLAN's master's thesis. In the thesis study, some phenotypic data (described in Materials section) obtained within the framework of the TUBITAK 1003 project numbered as 118O056 and 118O059 titled as Establishment of a Marker-Qtl Based Panel System for Marker Assisted Selection for Some Yield Traits in Pure Line Layers were used. The authors of the article (Eftal SEHIRLI and Kubra ARSLAN) thank the project coordinators Dr. Huseyin GOGER and Prof. Dr. Seyit Ali KAYIS.en_US
dc.identifier.doi10.1016/j.eswa.2022.117692
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85131461365en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.117692
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4630
dc.identifier.volume205en_US
dc.identifier.wosWOS:000832957100009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectEgg qualityen_US
dc.subjectHaugh uniten_US
dc.subjectMachine learningen_US
dc.titleAn application for the classification of egg quality and haugh unit based on characteristic egg features using machine learning modelsen_US
dc.typeArticleen_US

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