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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9269550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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