Mosquito Species Classification through Wingbeat Analysis: A Hybrid Machine Learning Approach

The potential of utilizing the features of wingbeat sound to detect different species of flying mosquitoes is explored in this paper. By analyzing the environmental sound using machine learning, it becomes possible to identify as well as classify different species of flying mosquito before it spreads mosquito-borne diseases. To accomplish the identification and classification, this paper presents a hybrid model that combines Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to analyze the audio in order to classify different species. The proposed model shows significant accuracy for detecting and classifying different species of flying mosquitoes as well as mitigate the weakness of individual models. If the hybrid model is widely used, could help to reduce the spread of mosquito-borne diseases and related fatalities.