Su Yin M.Gunatilaka D.Assawavinijkulchai K.Thampakorn T.Heesawat W.Haddawy P.Eiamsamang S.Sriwichai P.Weber M.Mahidol University2023-10-262023-10-262023-09-06ACM International Conference Proceeding Series (2023) , 285-290https://repository.li.mahidol.ac.th/handle/20.500.14594/90799Diseases transmitted by mosquito vectors, such as malaria, dengue, and Zika virus, pose significant healthcare challenges worldwide. Accurately estimating mosquito populations is vital for understanding transmission risks. Previous studies have explored the use of mosquito wingbeats for population surveys and control efforts. However, these methods require extensive data collection and annotation, which is time-consuming and resource-intensive. Additionally, laboratory-collected datasets lack biodiversity information from wild mosquitoes. To overcome these limitations, we propose an IoT system that automates the collection of mosquito wingbeat sounds in the field. Our system integrates with the Biogents BG-Counter 2 smart mosquito trap and incorporates a cost-effective acoustic sensing device. Initial assessments indicate successful integration, seamless data transmission, and satisfactory audio quality for classifying mosquito wingbeats. This solution offers an efficient alternative for gathering wingbeat data from species naturally present in the field.Computer ScienceConference PaperSCOPUS10.1145/3582515.36095462-s2.0-85174285701