Hybrid Machine Learning Approach for Mosquito Species Classification Using Wing beat Analysis

Abstract

Global public health continues to face substantial obstacles from mosquito-borne diseases, making precise and effective techniques for mosquito species identification necessary. We present a unique method in this article called "Mosquito Species Classification through Wingbeat Analysis: A Hybrid Machine Learning Approach," which uses wingbeat analysis and deep learning techniques to classify mosquito species. Our hybrid methodology attempts to provide robust and dependable classification performance by utilizing a wide range of machine learning methods, such as k-Nearest Neighbors (KNN), Random Forest, Multi-layer Perceptron (MLP), Support Vector Machines (SVM), Gradient Boosting, and Random Forest. We make use of an extensive dataset that includes wingbeat recordings from many species of mosquitoes, and we apply strict preprocessing techniques to improve feature extraction and normalization. After a thorough testing and assessment, we show that our method is more effective than separate algorithms at correctly classifying different species of mosquitoes. Our findings demonstrate the potential of deep learning methods to supplement conventional machine learning techniques in problems involving the classification of mosquito species. Additionally, we highlight the significance of precise species identification in vector surveillance and epidemiological research as we examine the implications of our findings for ecological studies and disease control initiatives.