Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques

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dc.authoridAltuntaş, Yahya/0000-0002-7472-8251
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridCengiz, Rahime/0000-0001-6355-7496
dc.authorwosidCömert, Zafer/F-1940-2016
dc.authorwosidAltuntaş, Yahya/AAH-8390-2019
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidCengiz, Rahime/V-2991-2019
dc.authorwosidCengiz, Rahime/AAC-3125-2019
dc.contributor.authorAltuntas, Yahya
dc.contributor.authorKocamaz, Adnan Fatih
dc.contributor.authorComert, Zafer
dc.contributor.authorCengiz, Rahime
dc.contributor.authorEsmeray, Mesut
dc.date.accessioned2024-08-04T20:45:46Z
dc.date.available2024-08-04T20:45:46Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractDoubled haploid (DH) technique is used effectively in maize breeding. This technique is superior to conventional maize breeding in terms of both time and homozygosity. One of the important processes in DH technique is the selection of haploid seeds. The most common method for selecting haploids is the RI-nj (Navajo) color marker. This color marker appears in the seed endosperm and embryo. Only endosperm color seeds are selected and continued to the germination stage This selection is usually done manually. The automation of haploid seed selection will increase success and reduce the labor and time In this study, we used 87 haploid and 326 diploid maize seeds as dataset. Texture features of maize seeds embryos were used These features were obtained from gray level co-occurrence matrix. The feature vectors are classified using decision trees, k-nearest neighbors and artificial neural networks. The classification performance of machine learning tecniques was tested by using 10 fold cross-validation method As a result of the test, the best performance was measured in decision tree with the classification success rate as 84.48%.en_US
dc.description.sponsorshipInonu Univ, Comp Sci Dept,IEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-6878-8
dc.identifier.scopus2-s2.0-85062520381en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98689
dc.identifier.wosWOS:000458717400021en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectmaizeen_US
dc.subjecthaploid identificationen_US
dc.subjecttexture featuresen_US
dc.subjectGLCMen_US
dc.subjectdecision treeen_US
dc.subjectkNNen_US
dc.subjectANNen_US
dc.titleIdentification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniquesen_US
dc.typeConference Objecten_US

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