Computational Models for Identifying Aedes aegypti Breeding Sites in Aerial Images: A Systematic Review
Resumo
As arboviroses transmitidas pelo mosquito Aedes aegypti representam desafios à saúde pública, principalmente em regiões tropicais. Este artigo apresenta uma Revisão Sistemática da Literatura sobre o uso de modelos para identificação de criadouros a partir de imagens aéreas. Seguindo as diretrizes PRISMA, foram selecionados 11 estudos publicados entre 2020 e 2026. Os resultados indicam predominância do uso de deep learning, especialmente modelos convolucionais como YOLO, com desempenho consistente. São discutidos as limitações observadas e os trabalhos mais robustos da área.Referências
Amarasinghe, A. and Wijesuriya, V. B. (2020). Drones vs dengue: A drone-based mosquito control system for preventing dengue. In Proceedings - 2020 RIVF International Conference.
Apu, M. S. H., Ahmed, S., and Ahmed, M. T. (2025). Smart system for real time monitoring and diagnosis of dengue surfaces in bangladesh. Array.
Carrasco-Escobar, G., Moreno, M., Fornace, K., Herrera-Varela, M., Manrique, E., and Conn, J. E. (2022). The use of drones for mosquito surveillance and control. Parasites & vectors, 15(1):473.
Codeco, C., Coelho, F., Cruz, O., Oliveira, S., Castro, T., and Bastos, L. (2018). Infodengue: A nowcasting system for the surveillance of arboviruses in brazil. Revue d’Épidémiologie et de Santé Publique, 66:S386. European Congress of Epidemiology “Crises, epidemiological transitions and the role of epidemiologists”.
Deb, A., Ahmed, S. I., Islam, A., Haque, M., Fattah, S. A., and Shahnaz, C. (2023). A real-time automatic dengue breeding zone detection and prevention scheme based on mobilenetv2 enabled autonomous drone. In 26th International Conference on Computer and Information Technology (ICCIT), pages 1–6.
Gao, Z. et al. (2025). Detection of potential breeding sites based on aav remote sensing imagery and multidccsp-yolo network. IEEE Access, 13.
Hassan, M. M., Chamberlin, A. J., Tarpenning, M. S., Weber, W. C., Coombe, K. D., De Leo, G. A., Junaid, M., Soma, A. S., Ansariadi, and Rosser, J. I. (2026). Enhancing aedes aegypti breeding site detection: A deep learning approach to tire identification in unmanned aerial vehicle imagery from urban indonesia. Remote Sensing Applications: Society and Environment.
Laranjeira, C., Andrade, D., and dos Santos, J. A. (2023). Yolov7 for mosquito breeding grounds detection and tracking. In IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW), pages 1–5.
Lima, G. A., Cotrin, R. O., Belan, P. A., and de Araújo, S. A. (2021). Computer vision system for automatic identification of potential aedes aegypti mosquito breeding sites using drones. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Group, P., et al. (2010). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. International journal of surgery, 8(5):336–341.
Mylvaganam, P. and Dissanayake, M. B. (2022). Deep learning for arbitrary-shaped water pooling region detection on aerial images. In MERCon 2022 - Moratuwa Engineering Research Conference.
Nyambo, D., Lyimo, T. M., and Sauli, E. (2026). High-resolution uav dataset for identification of breeding sites for malaria and dengue vectors in rural and peri-urban tanzania: An experimental approach with gemma 3n e4b architecture. Data in Brief.
Passos, W. L., Araujo, G. M., de Lima, A. A., Netto, S. L., and da Silva, E. A. (2022). Automatic detection of aedes aegypti breeding grounds based on deep networks with spatio-temporal consistency. Computers, Environment and Urban Systems, 93:101754.
Pinto, A. O., Ciarelli, P. M., and Sarcinelli-Filho, M. (2021). Creating a yolov3 custom model for aerial surveillance. IEEE.
Scott, T. W. and Takken, W. (2012). Feeding strategies of anthropophilic mosquitoes result in increased risk of pathogen transmission. Trends in parasitology, 28(3):114–121.
Sutanto, H. and Ansharullah, B. A. (2025). The role of artificial intelligence for dengue prevention, control, and management: A technical narrative review. Acta Tropica, 268:107741.
Thevarasa, N., Ananthajothy, G., Navaratnam, R., and Dissanayake, M. B. (2023). Weighted ensemble algorithm for aerial imaging based mosquito breeding sites classification. In 2023 IEEE 17th International Conference.
Apu, M. S. H., Ahmed, S., and Ahmed, M. T. (2025). Smart system for real time monitoring and diagnosis of dengue surfaces in bangladesh. Array.
Carrasco-Escobar, G., Moreno, M., Fornace, K., Herrera-Varela, M., Manrique, E., and Conn, J. E. (2022). The use of drones for mosquito surveillance and control. Parasites & vectors, 15(1):473.
Codeco, C., Coelho, F., Cruz, O., Oliveira, S., Castro, T., and Bastos, L. (2018). Infodengue: A nowcasting system for the surveillance of arboviruses in brazil. Revue d’Épidémiologie et de Santé Publique, 66:S386. European Congress of Epidemiology “Crises, epidemiological transitions and the role of epidemiologists”.
Deb, A., Ahmed, S. I., Islam, A., Haque, M., Fattah, S. A., and Shahnaz, C. (2023). A real-time automatic dengue breeding zone detection and prevention scheme based on mobilenetv2 enabled autonomous drone. In 26th International Conference on Computer and Information Technology (ICCIT), pages 1–6.
Gao, Z. et al. (2025). Detection of potential breeding sites based on aav remote sensing imagery and multidccsp-yolo network. IEEE Access, 13.
Hassan, M. M., Chamberlin, A. J., Tarpenning, M. S., Weber, W. C., Coombe, K. D., De Leo, G. A., Junaid, M., Soma, A. S., Ansariadi, and Rosser, J. I. (2026). Enhancing aedes aegypti breeding site detection: A deep learning approach to tire identification in unmanned aerial vehicle imagery from urban indonesia. Remote Sensing Applications: Society and Environment.
Laranjeira, C., Andrade, D., and dos Santos, J. A. (2023). Yolov7 for mosquito breeding grounds detection and tracking. In IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW), pages 1–5.
Lima, G. A., Cotrin, R. O., Belan, P. A., and de Araújo, S. A. (2021). Computer vision system for automatic identification of potential aedes aegypti mosquito breeding sites using drones. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Group, P., et al. (2010). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. International journal of surgery, 8(5):336–341.
Mylvaganam, P. and Dissanayake, M. B. (2022). Deep learning for arbitrary-shaped water pooling region detection on aerial images. In MERCon 2022 - Moratuwa Engineering Research Conference.
Nyambo, D., Lyimo, T. M., and Sauli, E. (2026). High-resolution uav dataset for identification of breeding sites for malaria and dengue vectors in rural and peri-urban tanzania: An experimental approach with gemma 3n e4b architecture. Data in Brief.
Passos, W. L., Araujo, G. M., de Lima, A. A., Netto, S. L., and da Silva, E. A. (2022). Automatic detection of aedes aegypti breeding grounds based on deep networks with spatio-temporal consistency. Computers, Environment and Urban Systems, 93:101754.
Pinto, A. O., Ciarelli, P. M., and Sarcinelli-Filho, M. (2021). Creating a yolov3 custom model for aerial surveillance. IEEE.
Scott, T. W. and Takken, W. (2012). Feeding strategies of anthropophilic mosquitoes result in increased risk of pathogen transmission. Trends in parasitology, 28(3):114–121.
Sutanto, H. and Ansharullah, B. A. (2025). The role of artificial intelligence for dengue prevention, control, and management: A technical narrative review. Acta Tropica, 268:107741.
Thevarasa, N., Ananthajothy, G., Navaratnam, R., and Dissanayake, M. B. (2023). Weighted ensemble algorithm for aerial imaging based mosquito breeding sites classification. In 2023 IEEE 17th International Conference.
Publicado
19/10/2026
Como Citar
FARIAS, Rafaela R.; SILVA, André G. C.; RODRIGUES, Geovana de S.; MAGALHÃES, Eduardo R. M.; MOTA, Milena B. B. da; OLIVEIRA, Mariane S.; SILVA, Camilly V. L. da; LIMA, Hélder S..
Computational Models for Identifying Aedes aegypti Breeding Sites in Aerial Images: A Systematic Review. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2551-2562.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.29924.
