Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification

Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) emerges as a precise and accessible method, excelling in species differentiation through mathematical approaches. Computational technologies and Artificial Intelligence (AI) promise to overcome WGM challenges, supporting mosquito identification. AI explores computers' thinking capacity, originating in the 1950s. Machine Learning (ML) arose in the 1980s as a subfield of AI, and deep Learning (DL) characterizes ML's subcategory, featuring hierarchical data processing layers. DL relies on data volume and layer adjustments. Over the past decade, AI demonstrated potential in mosquito identification. Various studies employed optical sensors, and Convolutional Neural Networks (CNNs) for mosquito identification, achieving average accuracy rates between 84 % and 93 %. Furthermore, larval Aedes identification reached accuracy rates of 92 % to 94 % using CNNs. DL models such as ResNet50 and VGG16 achieved up to 95 % accuracy in mosquito identification. Applying CNNs to georeference mosquito photos showed promising results. AI algorithms automated landmark detection in various insects' wings with repeatability rates exceeding 90 %. Companies have developed wing landmark detection algorithms, marking significant advancements in the field. In this review, we discuss how AI and WGM are being combined to identify mosquito species, offering benefits in monitoring and controlling mosquito populations.