Abstract:
The outbreak of mosquito-borne diseases such as malaria, dengue, chikungunya, Zika, yellow fever, and lymphatic filariasis has become a major threat to human existence. Hence, the elimination of harmful mosquito species has become a worldwide necessity. The techniques to reduce and eliminate these mosquito species require the monitoring of their populations in regions across the globe. This monitoring can be performed by automatic detection from the sounds of their wingbeats, which can be recorded in mosquito suction traps. In this paper, using the sounds emitted from their wingbeats, we explore the detection of the six most harmful mosquito species. From 279,566 wingbeat recordings in the wingbeat kaggle dataset, we balance the data across the six mosquito species using data augmentation techniques. With the use of state-of-the-art machine learning models, we achieve detection accuracies of up to 97%. These models can then be integrated with mosquito suction traps to form an efficient mosquito species detection system.