AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot

Abstract

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.

Considerations for first field trials of low-threshold gene drive for malaria vector control

“…Numerous AI, machine learning, and deep learning approaches are also currently being developed to allow the identification, and potentially counting [107], in the field of mosquito species at adult [117][118][119][120][121][122][123][124][125][126][127] and aquatic stages [127], using morphological characteristics, or even wingbeat patterns [126], which could be augmented by DNA barcoding analyses [128]. These approaches might also be combined with the use of UAVs to conduct mosquito vector population surveillance with minimal human resource implications [107].…”

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