Accurate taxonomic identification of parasitoid wasps is crucial for biodiversity assessment, ecological monitoring, and biological control programs. Due to morphological similarities, small size, and fine interspecific variations, manual identification is labor-intensive and expertise-dependent. This study proposes a deep learning-based framework for the automated identification of Ichneumonoidea wasps using a YOLO-based architecture integrated with High-Resolution Class Activation Mapping (HiResCAM) to enhance interpretability.
The proposed system identifies wasp families from high-resolution images. The dataset comprises 3556 high-resolution images of Hymenoptera specimens, mainly concentrated in the families Ichneumonidae (n = 786), Braconidae (n = 648), Apidae (n = 466), and Vespidae (n = 460). Extensive experiments were conducted using a curated dataset, and model performance was evaluated through precision, recall, F1 score, and accuracy. Results demonstrate high accuracy over 96% and robust generalization across morphological variations.
HiResCAM visualizations confirm that the model focuses on taxonomically relevant anatomical regions, such as wing venation, antenna segmentation, and metasomal structures, validating the biological plausibility of the learned features. The integration of explainable AI techniques improves transparency and trustworthiness, making the system suitable for entomological research to accelerate biodiversity characterization in an under-described parasitoid superfamily.
Blogger's Review: This study presents an innovative solution for traditional insect classification by integrating deep learning with explainable AI, showcasing the technology's immense potential in biodiversity research. The efficiency of the YOLO architecture combined with the interpretability of HiResCAM effectively enhances classification accuracy while providing a solid foundation of trust for scientific inquiry.