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Article

AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot

Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
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Author to whom correspondence should be addressed.
Academic Editor: Hyun Myung
Sensors 2022, 22(13), 4921; https://doi.org/10.3390/s22134921
Received: 4 April 2022 / Revised: 28 April 2022 / Accepted: 6 May 2022 / Published: 29 June 2022
(This article belongs to the Section Sensors and Robotics)
Mosquito-borne diseases can pose serious risks to human health. Therefore, mosquito surveillance and control programs are essential for the wellbeing of the community. Further, human-assisted mosquito surveillance and population mapping methods are time-consuming, labor-intensive, and require skilled manpower. This work presents an AI-enabled mosquito surveillance and population mapping framework using our in-house-developed robot, named ‘Dragonfly’, which uses the You Only Look Once (YOLO) V4 Deep Neural Network (DNN) algorithm and a two-dimensional (2D) environment map generated by the robot. The Dragonfly robot was designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The YOLO V4 was trained with three mosquito classes, namely Aedes aegypti, Aedes albopictus, and Culex, to detect and classify the mosquito breeds from the mosquito glue trap. The efficiency of the mosquito surveillance framework was determined in terms of mosquito classification accuracy and detection confidence level on offline and real-time field tests in a garden, drain perimeter area, and covered car parking area. The experimental results show that the trained YOLO V4 DNN model detects and classifies the mosquito classes with an 88% confidence level on offline mosquito test image datasets and scores an average of an 82% confidence level on the real-time field trial. Further, to generate the mosquito population map, the detection results are fused in the robot’s 2D map, which will help to understand mosquito population dynamics and species distribution. View Full-Text
Keywords: robot; mosquito surveillance; deep learning; computer vision; mapping robot; mosquito surveillance; deep learning; computer vision; mapping
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MDPI and ACS Style

Semwal, A.; Melvin, L.M.J.; Mohan, R.E.; Ramalingam, B.; Pathmakumar, T. AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot. Sensors 2022, 22, 4921. https://doi.org/10.3390/s22134921

AMA Style

Semwal A, Melvin LMJ, Mohan RE, Ramalingam B, Pathmakumar T. AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot. Sensors. 2022; 22(13):4921. https://doi.org/10.3390/s22134921

Chicago/Turabian Style

Semwal, Archana, Lee Ming Jun Melvin, Rajesh Elara Mohan, Balakrishnan Ramalingam, and Thejus Pathmakumar. 2022. "AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot" Sensors 22, no. 13: 4921. https://doi.org/10.3390/s22134921

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