Global climate change and increased international travel have affected the transmission of mosquito-borne diseases. In South Korea, uncommon diseases such as Dengue, chikungunya and Zika virus could be transmitted by potent mediator like Aedes albopictus. In order to cope with the risk of mosquito-borne diseases, rapid mosquito monitoring system is needed. Current mosquito monitoring procedures include installation of outdoor traps-mosquito collection-species classification-analysis of disease detection – upload of information to government research institutes – disease alert. In this process, species classification takes a lot of time, and if we reduce the time, we can cope with the disease outbreak more quickly. In this study, we developed automate species classification system target for 5 mosquito species (Culex pipiens, Cx. tritaeniorhynchus, Ae. albpictus, Ae. vexans, Anopheles spp.) disease vector live in South Korea. After modeling the morphology of each mosquito species, machine learning was carried out using DenseNet (Densely Connected Networks), one of the models of Artificial Neural Network. Using the learned model, we tested the classification of 5 species of mosquitoes and showed the accuracy from 97.35% to 99.48% at the maximum. Future research will focus on increasing the number of identifiable mosquito species and reducing the time spent on species classification. The autonomous classification of mosquito species using Deep Learning technology will contribute to the development of mosquito monitoring system and public health.

• Dong In Kim(Department of Life Sciences, Incheon National University, Convergence Research Center for Insect Vectors, Incheon National University)
• Gwang Rae Noh(Department of Life Sciences, Incheon National University, Convergence Research Center for Insect Vectors, Incheon National University)
• Jun Young Park(Convergence Research Center for Insect Vectors, Incheon National University, Department of Embedded Systems Engineering, Incheon National University)
• Woo chul Kang(Convergence Research Center for Insect Vectors, Incheon National University, Department of Embedded Systems Engineering, Incheon National University)
• Hyung Wook Kwon(Department of Life Sciences, Incheon National University, Convergence Research Center for Insect Vectors, Incheon National University)