# Automated identification of larval mosquito species with computer vision

> **NIH NIH R43** · VECTECH, LLC · 2024 · $185,640

## Abstract

Abstract. Mosquitoes kill nearly half-a-million people each year. Due to lack of effective
vaccines for most mosquito-borne diseases, prevention of mosquito bites remains the primary
focus of disease mitigation. Larval surveillance - monitoring potential breeding sites to
understand larval species composition, abundance, and spatial distribution - is key to enabling
precision vector control. If medically relevant larvae are detected, targeted larvicide treatments
can eliminate the population prior to emergence as adult mosquitoes, when disease
transmission occurs. Unfortunately, due to its resource-intensive nature, few mosquito control
organizations (MCOs) have the capacity to conduct the full-process of larval surveillance with
identification to species. Technicians need to travel to the potential breeding site, collect
specimens, return to the lab for visual morphological identification under a microscope, and if
medically relevant species are found, return to the field for larvicide treatments. Delayed,
inaccurate, or missing larval species data can miss an opportunity for intervention prior to
emergence of adults, or misinform costly unnecessary treatment. While significant efforts have
been made to explore crowdsourcing larval data and unmanned aircraft systems (UAS) for rapid
assessments of potential breeding sites, implementation and technical challenges have limited
practical use of these approaches. No automated solutions exist to build larval species
identification capacities. We propose to develop the first commercially available image
recognition platform for operational larval surveillance of the 20 most relevant mosquito species
in the US. This proposal will first validate the optical requirements for species identification of
larvae. This will enable the development of a standardized imaging configuration that will be
used to build a high-quality image dataset of mosquito larvae of different species capturing
diagnostic morphological features. The image dataset will be used to train a computer vision
system to identify species of mosquito larvae. Ultimately the approaches developed here will
enable new larval surveillance products, from an identification field-tool to inform technician
larviciding at the point of specimen collection, to remote sensing systems that will continuously
monitor historical breeding sites and alert mosquito control organizations (MCOs) when
intervention is needed.

## Key facts

- **NIH application ID:** 10931913
- **Project number:** 1R43AI184319-01
- **Recipient organization:** VECTECH, LLC
- **Principal Investigator:** Roy Faiman
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $185,640
- **Award type:** 1
- **Project period:** 2024-04-16 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931913

## Citation

> US National Institutes of Health, RePORTER application 10931913, Automated identification of larval mosquito species with computer vision (1R43AI184319-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10931913. Licensed CC0.

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