First application of two distinguishment techniques: Using Linear Discriminate Function method and Artificial Neural Networks approach according to the ovary types for some plant parasitic nematodes

In this study mono and dual ovaries, which belonged to female individuals of different plant parasitic nematode species that were obtained from the quince (Cydonia oblonga Mill.) (Rosales: Rosaceae) cultivated areas in Sakarya Province (Turkey), classified. The total number 109 121 nematodes, taken soil, used 2016, July 2017, July, respectively. Overall body length (L), spear (Stylet) tail/distance vulva anus (T/VA) parameters these nematodes measured examined. ovary groups distinguished by using Linear Discriminate Function (LDF) method (Fisher’s method) Artificial Neural Networks (ANNs) approach taking correlation between those into consideration. pair L had higher accuracy percentage (as 97% for LDF 100% ANNs approach) than Stylet 91% classification data set. second was more successful first method. This research is together at nematology area Turkey World. taxonomical studies may be improved statistical methods artificial neural networks approaches nematology.