Use of Support Vector Machines and Artificial Neural Network Methods in Variety Improvement Studies: Potato Example

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.