Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines

In 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%.