Classification of Apis cerana Populations Using Deep Learning Based on Morphometrics of Forewing in Thailand

This study aimed to develop a robust morphometric-based framework for classifying Apis cerana populations using deep learning and machine learning approaches. Previous studies on Apis cerana population differentiation have primarily relied on manual morphometrics or genetic markers, which are labor-intensive and often lack scalability for large image-based datasets. Forewing landmarks were automatically detected through a deep learning model employing a heatmap regression and Hourglass Network architecture. The extracted coordinates were processed by Principal Component Analysis (PCA) for dimensionality reduction, and shape alignment was further refined through Procrustes ANOVA to minimize non-biological variation. Nine machine learning algorithms were trained and compared under identical preprocessing and validation settings. Among them, the Extra Trees classifier achieved the highest accuracy (99.7%) in distinguishing the three populations—A. cerana cerana from China and A. cerana indica from Thailand, the northern and southern populations. After applying error-based data filtering and retraining, classification accuracy improved further, with almost perfect population separation. The Procrustes ANOVA confirmed that individual variation significantly exceeded residual error (Pillai’s trace = 1.13, p < 0.0001), validating the biological basis of shape differences. Mahalanobis distance and permutation tests (10,000 rounds) revealed significant morphological divergence among populations (p < 0.0001). The integration of geometric alignment and ensemble learning demonstrated a highly reliable strategy for population identification, supporting morphometric and evolutionary studies in Apis cerana.