AIDeM: Artificial Intelligence-based Dengue Mosquito Catching Device

Authors

  • Art Riguer Salmorin College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author
  • Trixia Allana D. Dela Llave College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author
  • Kent Francis Pon-An College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author
  • Redjie Serilla College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author
  • Gerard James B. Paglingayen College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author
  • Genesis V. Canja College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author
  • Sammy V. Militante College of Engineering and Architecture, University of Antique, Sibalom, Antique, Philippines Author

DOI:

https://doi.org/10.69478/BEST2025v1n2a032

Keywords:

Aedes Mosquito, AIDeM, Raspberry Pi, YOLOv8, Artificial Intelligence

Abstract

Dengue, carried by Aedes Aegypti, has been a world problem that caused severe fevers and even death. This study uses the latest computing techniques and technologies, like YOLOv8, Raspberry Pi, MongoDB, and yeast-sugar mixture to make a machine that captures mosquitoes. YOLOv8 as the Artificial Intelligence integrated in Raspberry Pi works well in capturing and getting data from captured mosquitoes. Whereas MongoDB is sufficient for saving gathered data. However further improvements are needed on the machine for effective operation. Monitoring the cause of dengue, Ae. Aegypti is important to gather data on its behavior and changes. 

Published

2025-07-19

How to Cite

AIDeM: Artificial Intelligence-based Dengue Mosquito Catching Device. (2025). Business, Education, Social Sciences, and Technology, 1(2). https://doi.org/10.69478/BEST2025v1n2a032

Similar Articles

1-10 of 17

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)