TY  - JOUR
TI  - 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
AB  - 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), were classified. The total number of 109 and 121 female nematodes, which were taken from the soil, were used in 2016, July and 2017, July, respectively. Overall body length (L), spear length (Stylet) and tail/distance from vulva to anus (T/VA) parameters belonged to these nematodes were measured and examined. The mono and dual ovary groups were distinguished by using the Linear Discriminate Function (LDF) method (Fisher’s method) and Artificial Neural Networks (ANNs) approach taking correlation between those parameters into consideration. The pair of parameters L and (T/VA) had higher accuracy percentage (as 97% for LDF method and 100% for ANNs approach) than the pair of parameters L and Stylet (as 91% for LDF method and 97% for ANNs approach) for the classification using 2017, July data set. The second approach was more successful than the first method. This research is the first study that was used these method and approach together at the nematology study area in Turkey and the World. The taxonomical studies may be improved using different statistical methods and artificial neural networks approaches together at the nematology.
AU  - Tan, Aylin
AU  - Tan, Ayşe Nur
AU  - SUSURLUK, HİLAL
DO  - 10.29050/harranziraat.1025087
PY  - 2022
JO  - Harran Tarım ve Gıda Bilimleri Dergisi
VL  - 26
IS  - 1
SN  - 2587-1358
SP  - 1
EP  - 14
DB  - TRDizin
UR  - http://search/yayin/detay/1124815
ER  -