모기류는 흡혈을 통해 원충, 바이러스, 사상충 등 다양한 병원체를 보유하며 말라리아, 일본뇌염, 웨스트나일열, 뎅기열 등을 사람에게 매 개하는 위해성이 있는 위생해충이다. 이번 연구에서는 해외유입 모기류 감시를 위해 경상남도 고성군에 스마트 고공포집기를 설치하여 2022년 부터 2023년까지 모기류들을 모니터링하였다. 조사기간 동안, 총 3속 5종 43개체가 채집되었으며, 이중 삼일열말라리아를 매개하는 벨렌얼룩날 개모기(Anopheles belenrae)를 경남 지역에서 처음으로 발생함을 확인하였다. 본 연구에서는 스마트 고공포집기를 통해 해외에서 유입가능한 모 기류에 대한 실시간 모니터링이 가능함을 확인하였다.

Due to climate change and the rise in international transportation, there is an emerging potential for outbreaks of mosquito-borne diseases such as malaria, dengue, and chikungunya. Consequently, the rapid detection of vector mosquito species, including those in the Aedes, Anopheles, and Culex genera, is crucial for effective vector control. Currently, mosquito population monitoring is manually conducted by experts, consuming significant time and labor, especially during peak seasons where it can take at least seven days. To address this challenge, we introduce an automated mosquito monitoring system designed for wild environments. Our method is threefold: It includes an imaging trap device for the automatic collection of mosquito data, the training of deep-learning models for mosquito identification, and an integrated management system to oversee multiple trap devices situated in various locations. Using the well-known Faster-RCNN detector with a ResNet50 backbone, we’ve achieved mAP (@IoU=0.50) of up to 81.63% in detecting Aedes albopictus, Anopheles spp., and Culex pipiens. As we continue our research, our goal is to gather more data from diverse regions. This not only aims to improve our model’s ability to detect different species but also to enhance environmental monitoring capabilities by incorporating gas sensors.