A University of Wollongong (UOW) academic has developed a small artificial intelligence device that can identify disease-carrying mosquitoes by analysing the sound of their wingbeats, offering a faster and more accessible approach to mosquito monitoring.
Associate Professor Kiran Trivedi designed the portable system using Tiny Machine Learning (TinyML), a technology that allows AI models to operate on small, low-power devices without relying on internet connections or cloud computing. The device can recognise three major mosquito groups linked to disease transmission: Aedes, Anopheles and Culex.
Associate Professor Trivedi has been invited to present the technology at the United Nations AI for Good Global Summit in Geneva this month, where researchers and technology leaders will discuss how artificial intelligence can support global challenges.
Mosquitoes remain a major public health concern worldwide, with diseases such as malaria and dengue affecting millions of people each year. Many remote and resource-limited communities face challenges in monitoring mosquito populations because current identification methods often require collecting samples and sending them to laboratories for analysis.
Pic supplied
Traditional surveillance methods remain widely used and provide accurate results, but they can take time. Associate Professor Trivedi’s approach uses the unique acoustic patterns created by mosquito wingbeats to identify species in seconds.
“When people think about AI, they imagine huge systems running in the cloud,” Associate Professor Trivedi said.
“TinyML lets us put the intelligence directly onto the device. It identifies the mosquito in seconds, with no internet, no cloud costs and no privacy concerns.”
The AI model was trained using publicly available mosquito sound recordings and achieved an accuracy rate of 88.3 per cent during testing. Associate Professor Trivedi said the results provide a strong foundation for further improvements through better microphones and higher-quality audio data.
The device is built using an Arduino-based system, a low-cost programmable circuit board commonly used for developing electronic prototypes. It includes a microphone and display, allowing it to process mosquito sounds directly on the device.
Associate Professor Trivedi said the technology could eventually support wider monitoring networks, with multiple devices collecting information about mosquito activity and helping health authorities identify areas where disease-carrying species may be increasing.
“Just as a navigation app shows you traffic in real time, this could show where disease-carrying mosquitoes are building up,” he said.
“Instead of waiting for an outbreak, communities and public health agencies could see the hotspots early and respond.”
The research was co-authored with former UOW student Harsh Shroff and was first published in 2021 through the International Telecommunication Union’s Kaleidoscope conference proceedings.
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