Classifying the gender of Epicometis (Tropinota) hirta poda (Coleoptera: Scarabaeidae) individuals via data mining algorithm

Tropinota (Epicometis) hirta (Poda) fruit trees, strawberries, roses, wheatgrass feed on the flowers of plants such as wheatgrass, eat male and female organs. Sometimes they feed on shoots, leaves or even fruit. Since their ability to fly is high, they migrate to different plants and continue their damage. As a result, damaged flowers cannot set fruit. They are important pests related to agriculture. This study aimed to develop a morphometric characterization method to determine the gender of the beetle species T. hirta using tree-based data mining algorithms based on certain taxonomic characteristics. A total of 516 female and 504 male individuals were used in the study, and their morphometric features were used as independent variables. The dependent variable for the data mining algorithms was the gender of the individuals, defined as a binary response variable. The performance of the algorithms- Chi-Square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART), and Quick Unbiased, Efficient Statistical Tree (QUEST)- in differentiating the sexes was evaluated using several metrics such as the area under the ROC curve, Matthews correlation coefficient, and Cohen's Kappa coefficient. The results showed that all algorithms were able to successfully distinguish the sexes of the individuals with high accuracy. The first branching of the tree structure generated by the algorithms was based on the Filagellum Length (FU) of the individuals, with specific cut-off points identified for each algorithm. The findings of this study demonstrated the potential of using morphometric characteristics and tree-based data mining algorithms to determine the sexes of T. hirta individuals, and suggested that such methods can be integrated into taxonomic studies to improve the accuracy of species identification.