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<p>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.</p>

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Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月29日

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  • 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

摘要

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.