AI-powered Mosquito Detection

AI-powered Mosquito Detection

This live demonstration provides an interactive experience of identifying mosquito species through their wingbeat sounds. A compact sensing module captures the mosquito buzz in real time, which is processed by a TinyML-based model running on low-power embedded hardware. Participants will be able to observe how the system captures mosquito buzz, extracts acoustic features, and instantly classifies mosquito species such as AedesAnopheles, and Culex. The results will be displayed live, allowing users to see how different wingbeat patterns correspond to different species. The demo setup includes a portable edge device, audio sensing unit, and a visualization interface that shows classification outputs in real time. Attendees can interact with the system, understand how sound-based detection works, and explore how AI models operate directly on-device without external connectivity. This demonstration highlights the practicality of deploying AI at the edge for real-world health applications. It offers a hands-on view of how simple acoustic signals can be transformed into meaningful insights for early detection and monitoring of disease-carrying mosquitoes.

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In person
10 July 2026
23:30 - 23:45
EST - New York
CST - Beijing
PST - Los Angeles
AWST - Perth, Australia

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