Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea

Simple Summary Conventional manual counting methods for the monitoring of mosquito species and populations can hinder the accurate determination of the optimal timing for pest control in the field. In this study, a deep learning-based automated image analysis method was developed for the classification of eleven species of mosquito. The combination of color and fluorescence images enhanced the performance for live mosquito classification. The classification result of a 97.1% F1-score has demonstrated the potential of using an automatic measurement of mosquito species and populations in the field. The proposed technique could be adapted for establishing a mosquito monitoring and management system, which may contribute to preemptive quarantine and a reduction in the exposure to vector-borne diseases. Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.