Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification
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 depende...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
29 April 2025
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| In: |
Scientific data
Year: 2025, Volume: 12, Issue: 1, Pages: 1-6 |
| ISSN: | 2052-4463 |
| DOI: | 10.1038/s41597-025-05043-3 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41597-025-05043-3 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41597-025-05043-3 |
| Author Notes: | Kristopher Nolte, Eric Agboli, Gabriela Azambuja 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, Stephanie Jansen, Artur Jöst, Hanna Jöst, Ellen Kiel, Alexandra Meyer, Wolf-Peter Pfitzner, Joy Saathoff, Jonas Schmidt-Chanasit, Tatiana Sulesco, Artin Tokatlian, Thirumalaisamy P. Velavan, Carmen Villacañas de Castro, Magdalena Laura Wehmeyer, Julien Zahouli, Felix Gregor Sauer & Renke Lühken |
| Summary: | 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. |
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| Item Description: | Gesehen am 07.11.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2052-4463 |
| DOI: | 10.1038/s41597-025-05043-3 |