A Vector Mosquitoes Classification System Based on Edge Computing and Deep Learning

In recent years, we have witnessed a sudden increase in mosquito-borne diseases and related casualties. This makes it important to have an efficient mosquito classification system. In this paper, we implement a mosquito classification system which is capable of identifying Aedes and Culex (types of the mosquito) automatically. To facilitate the implementation of such Internet of Things (IoT) based system, we first create a trap device with a stable area for filming mosquitoes. Then, we analyze video frames in order to reduce the video size for transmission. We also build a model to identify different types of mosquitoes using deep learning. Later, we fine-tune the edge computing on the trap device to optimize the system efficiency. Finally, we integrate the device and the model into a mosquito classification system and test the system in wild fields in Taiwan. The tests show significant results when the experiments are conducted in the rural area. We are able to achieve an accuracy of 98% for validation data and 90.5% for testing data.