Identification of gravid mosquitoes from changes in spectral and polarimetric backscatter cross sections

Adrien P. Genoud, Yunpeng Gao, Gregory M. Williams, Benjamin P. Thomas

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Improving the survey of mosquito populations is of the utmost importance to further enhance mitigation techniques that protect human populations from mosquito-borne diseases. While mosquito populations are generally studied using physical traps, stand-off optical sensors allow to study insect ecosystems with potentially better spatial and temporal resolution. This can be greatly beneficial to eco-epidemiological models and various mosquito control programs. In this contribution, we demonstrate that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections. Among other predictive variables, the wing beat frequency and the depolarization ratio of the mosquito body allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively. Since female mosquitoes need a blood meal to become gravid, statistics on gravidity is of prime importance as only females that have been gravid might carry infectious diseases. In addition, it allows to detect possible breeding habitat, predict a potential increase in the mosquito population and provide a better overall understanding of the ecosystem dynamics. As a result, targeted and localized mitigation techniques can be used, reducing the cost and improving the efficiency of mosquito population control.

Original languageEnglish (US)
Article numbere201900123
JournalJournal of Biophotonics
Volume12
Issue number10
DOIs
StatePublished - Oct 1 2019

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Engineering(all)
  • Physics and Astronomy(all)

Keywords

  • classification
  • entomology
  • gravid
  • lidar
  • mosquito
  • remote sensing

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