COMPARISON OF A STATISTICAL METHOD AND AN ARTIFICIAL INTELLIGENCE APPROACH IN TAXONOMICAL NEMATOLOGY FROM TURKIYE: USING A PAIR OF DETERMINED MORPHOMETRIC PARAMETERS

Abstract

In this study, mono and dual ovaries of plant parasitic nematodes in quince (Cydonia oblonga Mill.) (Rosales: Rosaceae) cultivated areas in Sakarya province of Turkiye, were investigated. A total of 230 female nematodes were used, which were obtained from the soil in July 2016 and 2017. The nematode which was examined exhibited the best relationship between the important parameters of the morphometric measurements. The mono and dual ovaries were discriminated by using the linear discriminate function (LDF) method and artificial neural networks (ANNs) approach. The pair of parameters were tried by using LDF method. Then it was observed that the pair of the tail length/tail diameter at anus or cloaca (c) and percentage of the distance of vulva from anterior (V%) parameters had the best correlation with each other considering the highest accuracy percentage obtained as 80% according to the LDF method. The c¢ versus (V%) of the nematode had a higher classification accuracy percentage for data set than others as 99% for LDF method and 91% for ANNs approach for the July 2016 set. Thus, it can be concluded that LDF method is as successful as ANNs approach.