*Corresponding author:MG Mustafayev, Department of Agriculture, Azerbaijan
Received: January 12, 2019; Published:January 29, 2019
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In order to make a contribution to the early generation selections in potato varieties through a classification, the MLPNN and SVM data mining methods were applied to the data set created by considering the selection criteria based on the macroscopic observations and measurements, performed to identify clones that are ineligible and to be eliminated through negative selection from the clones developed in line with the potato variety breeding program, initiated by hybrid combinations in this study. Data set used in the study consists of clones in a study conducted in 2016 as part of the project no. TUBITAK-TOVAG 113O928. A total of 703 potato clones from 12 hybrid combinations were used in the study. In order to identify the clones to be selected, two different models were created by using three attributes (tuber yield, number of tubers and average tuber weight) for each clone, and two different models were created by using two attributes (eyes depth and eyes pit depth) of each clone in order to identify the clones to be eliminated. Experiments were carried out by comparing the sensitivity, specificity and accuracy ratios for each model by using the generated dataset as input to the MLPNN and SVM classifiers. As a result of the experimental studies, the highest success was achieved in 2-class models and it was determined that MLPNN classifier is more successful in these models. With this study, it was put forth that data mining methods can be used for early generation selection in cultivar improvement studies.
Keywords: Potato; Clonal Selection; Breeding; Agricultural Information Technologies; Multilayer Perceptron Neural Network; Support Vector Machine
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