Open Access
Issue
E3S Web Conf.
Volume 688, 2026
The 2nd International Conference on Sustainable Environment, Development, and Energy (CONSER 2025)
Article Number 05002
Number of page(s) 8
Section Smart Technologies and Energy Solutions for a Low-Carbon Future
DOI https://doi.org/10.1051/e3sconf/202668805002
Published online 20 January 2026
  1. World Health Organization: WHO, Dengue and severe dengue, (2024). https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue [Google Scholar]
  2. A. Amarasinghe et al., A Machine Learning Approach for Identifying Mosquito Breeding Sites via Drone Images, in SenSys ' 17: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, 1–2 (2017). doi: 10.1145/3131672.3136986 [Google Scholar]
  3. T. M. Penhollow, L. Torres, Impact of mosquito-borne diseases on global public health. Int. Phys. Med. Rehabil. J. 6, 19–20 (2021). doi:10.15406/ipmrj.2021.06.00273 [Google Scholar]
  4. K. Marczell et al., The macroeconomic impact of a dengue outbreak: Case studies from Thailand and Brazil. PLoS Negl. Trop. Dis. 18, e 0012201 (2024). doi:10.1371/journal.pntd.0012201 [Google Scholar]
  5. F. E. Edillo et al., Economic cost and burden of dengue in the Philippines. Am. J. Trop. Med. Hyg. 92, 360–366 (2014). doi:10.4269/ajtmh.14-0139 [Google Scholar]
  6. Y.-J. Huang, S. Higgs, D. Vanlandingham, Biological control strategies for mosquito vectors of arboviruses. Insects. 8, 21 (2017). doi:10.3390/insects8010021 [Google Scholar]
  7. F. Mechan, Z. Bartonicek, D. Malone, R. S. Lees, Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases. Malar. J. 22, 23–34 (2023). doi:10.1186/s12936-022-04179-3 [Google Scholar]
  8. J. Datta et al., Recognising Mosquito Breeding Zones Applying Machine Learning Over UAV Images: A Comparative Study between Dense Urban Area Urban Slum Area, in Routledge eBooks, (2023). doi:10.4324/9781003356233-13 [Google Scholar]
  9. K. Yu et al., Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus. Acta Trop. 255, 107234 (2024). doi: 10.1016/j.actatropica.2024.107234 [Google Scholar]
  10. R. Sapkota, D. Ahmed, M. Karkee, Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments. Artif. Intell. Agric. 13, 84–99 (2024). doi:10.1016/j.aiia.2024.07.001 [Google Scholar]
  11. S. Valladares et al., Performance Evaluation of the Nvidia Jetson Nano Through a Real-Time Machine Learning Application, in Intelligent Human Systems Integration. Advances in Intelligent Systems and Computing. 1322, 343–349 (2021). doi: 10.1007/978-3-030-68017-6 51 [Google Scholar]
  12. T.-N. Doan, T.-H. Phan, A Novel Smart System with Jetson Nano for Remote Insect Monitoring. Int. J. Adv. Comput. Sci. Appl. 15, (2024). doi:10.14569/ijacsa.2024.0150798 [Google Scholar]
  13. Drone Laws in the Philippines. UAV Coach (2021). https://uavcoach.com/drone-laws-in-philippines/ [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.