Integrating Developmental Traits, Morphometrics, and Machine Learning for Sex Classification of Aedes aegypti (Diptera: Culicidae)

This dataset contains morphometric measurements and machine learning classification results for Aedes aegypti pupae, generated to support research on sex differentiation in the context of Sterile Insect Technique (SIT) applications. Eight morphometric traits were recorded from pupal and adult stages, namely cephalothorax length, cephalothorax width, cephalothorax perimeter, cephalothorax area, pupal length, pupal weight, wing length, and thorax length. For machine learning analyses, six pupal parameters (cephalothorax length, cephalothorax width, cephalothorax perimeter, cephalothorax area, pupal length, and pupal weight) were used as predictive features for sex classification. The dataset includes raw morphometric measurements alongside performance metrics from multiple supervised algorithms, providing both trait-based and model-based perspectives on pupal sex differentiation.