Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification
Kristopher Nolte, Eric Agboli, Gabriela A. Garcia, Athanase Badolo, Norbert Becker, Do Huy Loc, Tarja Viviane Dworrak, Jacqueline Eguchi, Albert Eisenbarth, Rafael Maciel de Freitas, Ange Gatien Doumna-Ndalembouly, Anna Heitmann, Stéphanie Jansen, Artur Jöst, Hanna Jöst, Ellen Kiel, Alexandra Meyer, Wolf Peter Pfitzner, Joy Saathoff, Jonas Schmidt‐Chanasit, Tatiana Șuleșco, Artin Tokatlian, Thirumalaisamy P. Velavan, Carmen Villacañas de Castro, Magdalena Laura Wehmeyer, Julien Z. B. Zahouli, Felix Gregor Sauer, Renke Lühken
Abstract
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.