Donmez, Emrah2024-08-042024-08-042020978-1-7281-7206-42165-0608https://doi.org/10.1109/siu49456.2020.9302142https://hdl.handle.net/11616/9974728th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORKOne of the main processes in in vivo maternal haploid breeding method which is widely used in hybrid maize breeding in recent years is the separation of haploid and diploid individuals. Although different approaches have been proposed to make this distinction, the R1-nj color marker is widely and successfully used. The R1-nj color marker allows the visual separation of haploid and diploid individuals during the seed period. Nowadays, this distinction is done manually, causing loss of time and labor as well as high error. In this study, an open access dataset consisting of 3,000 maize seed images was used. The deep features from the FC6, FC7 and FC8 fully connected layers of the AlexNet architecture are classified with the support vector machine. 10-fold cross-validation test was used to evaluate model performances. Experimental results showed that the best classification performance is possible with 89.50% accuracy using deep features obtained from the FC6 fully connected layer.trinfo:eu-repo/semantics/closedAccesshaploid maize seed identificationimage processingconvolutional neural networksdeep featuresartificial learningConference Object10.1109/siu49456.2020.93021422-s2.0-85100289893N/AWOS:000653136100116N/A