Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification - Nolte, Kristopher; Agboli, Eric; Garcia, Gabriela Azambuja; Badolo, Athanase; Becker, Norbert; Loc, Do Huy; Dworrak, Tarja Viviane; Eguchi, Jacqueline; Eisenbarth, Albert; de Freitas, Rafael Maciel; Doumna-Ndalembouly, Ange Gatien; Heitmann, Anna; et al. (Scientific data, 2025)
Accurate identification of mosquito species is essential for effective vector control and mitigation of mosquito-borne disease outbreaks. Traditional morphological identification requires highly specialized personnel and is time-consuming, while molecular techniques can be cost-effective and dependent on comprehensive genetic information. Wing geometric morphometry has emerged as a promising alternative, leveraging detailed geometric measurements of wing shapes and vein patterns to distinguish between species and detect intraspecies variations. This paper presents a curated dataset of 18,104 mosquito wing images, collected from 10,500 mosquito specimens, annotated with extensive meta-information, designed to support research in wing geometric morphometry and the development of machine learning models, ultimately supporting efforts in vector surveillance and research.
Gesehen am 07.11.2025