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AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot

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 skille...

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Autores principales: Semwal, Archana, Melvin, Lee Ming Jun, Mohan, Rajesh Elara, Ramalingam, Balakrishnan, Pathmakumar, Thejus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269550/
https://www.ncbi.nlm.nih.gov/pubmed/35808427
http://dx.doi.org/10.3390/s22134921
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author Semwal, Archana
Melvin, Lee Ming Jun
Mohan, Rajesh Elara
Ramalingam, Balakrishnan
Pathmakumar, Thejus
author_facet Semwal, Archana
Melvin, Lee Ming Jun
Mohan, Rajesh Elara
Ramalingam, Balakrishnan
Pathmakumar, Thejus
author_sort Semwal, Archana
collection PubMed
description 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.
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spelling pubmed-92695502022-07-09 AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot Semwal, Archana Melvin, Lee Ming Jun Mohan, Rajesh Elara Ramalingam, Balakrishnan Pathmakumar, Thejus Sensors (Basel) Article 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. MDPI 2022-06-29 /pmc/articles/PMC9269550/ /pubmed/35808427 http://dx.doi.org/10.3390/s22134921 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Semwal, Archana
Melvin, Lee Ming Jun
Mohan, Rajesh Elara
Ramalingam, Balakrishnan
Pathmakumar, Thejus
AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot
title AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot
title_full AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot
title_fullStr AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot
title_full_unstemmed AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot
title_short AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot
title_sort ai-enabled mosquito surveillance and population mapping using dragonfly robot
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269550/
https://www.ncbi.nlm.nih.gov/pubmed/35808427
http://dx.doi.org/10.3390/s22134921
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