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    Classification of Haploid and Diploid Maize Seeds by Using Image Processing Techniques and Support Vector Machines
    (Ieee, 2018) Altuntas, Yahya; Kocamaz, Adnan Fatih; Cengiz, Rahime; Esmeray, Mesut
    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%.
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    Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques
    (Ieee, 2018) Altuntas, Yahya; Kocamaz, Adnan Fatih; Comert, Zafer; Cengiz, Rahime; Esmeray, Mesut
    Doubled 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%.