ArticlePLoS neglected tropical diseases2019
High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery.
Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Associations between weather and Plasmodium vivax malaria in an Amazonian elimination setting: a distributed lag analysis from 2017 to 2024.Malaria journal · 2026Article
- Deep learning identifies water bodies from low-cost drone images for mosquito larval habitat mapping.Scientific reports · 2026Article
- High-resolution automated mapping of potential Aedes larval container habitats using drone imagery and supervised machine learning in Dar es Salaam, Tanzania.PLoS neglected tropical diseases · 2026Article
- New frontiers for unmanned aerial vehicles in planetary health research.BMC global and public health · 2026Review
- Community participation and technological innovation: Baseline qualitative insights to inform a five-year cohort on drone-based dengue surveillance in Malaysia.PLoS neglected tropical diseases · 2026Article
- Sublethal Pyriproxyfen Exposure AltersInsects · 2026Article
- Article
- Horizons of the Future: Preparedness and Response.Current topics in microbiology and immunology · 2026Review
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- A large-scale mosquito larviciding in Tanga Region, Tanzania, reduced mosquito densities to varying degrees across malaria transmission risk strata.Scientific reports · 2025Article
- Submicroscopic infections and two malaria vectors are major contributors to residual malaria in an Amazonian village in Peru.Scientific reports · 2025Article
- Monitoring individual rice field flooding dynamics over a large scale to improve mosquito surveillance and control.Malaria journal · 2025Article
- Comparison of unmanned aerial vehicle imaging to ground truth walkthroughs for identifying and classifying trash sites serving as potential Aedes aegypti breeding grounds.Parasites & vectors · 2025Article
- Associations between weather andmedRxiv : the preprint server for health sciences · 2024Article
- The impact of Bacillus thuringiensis var. israelensis (VectobacMalaria journal · 2024Article
- Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.International journal of health geographics · 2024Article
- Cost and quality of operational larviciding using drones and smartphone technology.Malaria journal · 2023Article
- Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance.Remote sensing · 2023Article
- Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases.Malaria journal · 2023Review
- Challenge and opportunity for vector control strategies on key mosquito-borne diseases during the COVID-19 pandemic.Frontiers in public health · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.
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Registered trials
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