Classification of Haploid and Diploid Maize Seeds by Using Image Processing Techniques and Support Vector Machines

Basit Öğe Kaydı
dc.authoridAltuntaş, Yahya/0000-0002-7472-8251
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidAltuntaş, Yahya/AAH-8390-2019
dc.authorwosidCengiz, Rahime/V-2991-2019
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorAltuntas, Yahya
dc.contributor.authorKocamaz, Adnan Fatih
dc.contributor.authorCengiz, Rahime
dc.contributor.authorEsmeray, Mesut
dc.date.accessioned2024-08-04T20:57:23Z
dc.date.available2024-08-04T20:57:23Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.description.abstractIn vivo maternal haploid technique is now widely used in advanced maize breeding programs. This technique shortens the breeding period and increases the efficiency of breeding. One of the important processes in this breeding technique is the selection of haploid seeds. The fact that this selection is performed manually reduces the selection success and causes time and labor loss. For this reason, it is a need to develop automatic selection methods that will save time and labor and increase selection success. In this study, a method was proposed to classify haploid and diploid maize seeds by using image processing techniques and support vector machines. Firstly, each maize seed is segmented from its original image. Secondly, five different features were extracted for each maize seed. Finally, obtained features vector is classified by using support vector machines. The proposed method performance was tested by 10-fold cross-validation method. As a result of the test, the success rate of haploid maize seed classification was calculated as 94.25% and the success rate of diploid maize seed classification was 77.91%.en_US
dc.description.sponsorshipIEEE,Huawei,Aselsan,NETAS,IEEE Turkey Sect,IEEE Signal Proc Soc,IEEE Commun Soc,ViSRATEK,Adresgezgini,Rohde & Schwarz,Integrated Syst & Syst Design,Atilim Univ,Havelsan,Izmir Katip Celebi Univen_US
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/11616/102588
dc.identifier.wosWOS:000511448500653en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 26th Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecthaploiden_US
dc.subjectmaizeen_US
dc.subjectclassificationen_US
dc.subjectimage processingen_US
dc.subjectsupport vector machinesen_US
dc.titleClassification of Haploid and Diploid Maize Seeds by Using Image Processing Techniques and Support Vector Machinesen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu