Automated Identification of Ectatomma edentatum (Hymenoptera: Formicidae) using Supervised Algorithms

Abstract

Taxonomy constantly seeks alternatives to simplify and enhance the identification of living organisms. This study focuses on developing new tools for identifying ant species, aiming to address gaps in determining certain species that pose challenges for naming and study. Species identification can often be a time-consuming and intricate process. We aim to automate the identification process of Ectatomma edentatum (Roger, 1863), utilizing Machine Learning techniques to assess if efficiency can be improved and gaps in ant taxonomy reduced. We applied k-nearest neighbors (KNN) and Support Vector Classification (SVC) algorithms. Adapting these models to the dataset yielded excellent results, with both models demonstrating positive performance in classifying the ants. SVC achieved 100% accuracy, while KNN achieved 96%, affirming the effectiveness of these methods in ant identification. This study highlights the value of supervised algorithms in myrmecology, offering a valuable tool for taxonomy and species classification, ultimately providing accurate synthesis and prediction for species naming.