Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation.

Sensors (Basel, Switzerland)·2023
Same author

Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data-A Pilot Study.

Biomedicines·2022
Same author

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications.

Sensors (Basel, Switzerland)·2022
Same author

Object-of-Interest Perception in a Reconfigurable Rolling-Crawling Robot.

Sensors (Basel, Switzerland)·2022
Same author

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms.

Sensors (Basel, Switzerland)·2022
Same author

AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 5, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.4K

AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot.

Archana Semwal1, Lee Ming Jun Melvin1, Rajesh Elara Mohan1

  • 1Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered robot, Dragonfly, for efficient mosquito surveillance. It uses deep learning to accurately detect and map mosquito populations, improving public health efforts.

Keywords:
computer visiondeep learningmappingmosquito surveillancerobot

More Related Videos

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

8.6K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.3K

Related Experiment Videos

Last Updated: Sep 5, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.4K
Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

8.6K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.3K

Area of Science:

  • Robotics and Artificial Intelligence
  • Public Health Entomology
  • Environmental Monitoring

Background:

  • Mosquito-borne diseases present significant public health risks, necessitating effective surveillance and control programs.
  • Current manual mosquito surveillance methods are inefficient, requiring substantial time, labor, and expertise.
  • There is a need for automated and accurate systems for mosquito population mapping.

Purpose of the Study:

  • To develop and evaluate an AI-enabled framework for automated mosquito surveillance and population mapping.
  • To integrate a deep learning model with a robotic platform for real-time mosquito detection and classification.
  • To generate 2D population maps for understanding mosquito distribution and dynamics.

Main Methods:

  • Development of the 'Dragonfly' robot with a differential drive and mosquito trapping module.
  • Utilizing the You Only Look Once (YOLO) V4 Deep Neural Network (DNN) algorithm for mosquito detection and classification.
  • Training the YOLO V4 model on three mosquito classes (Aedes aegypti, Aedes albopictus, Culex) using images from glue traps.
  • Conducting offline and real-time field tests in diverse environments (garden, drain perimeter, car parking).

Main Results:

  • The YOLO V4 DNN model achieved an 88% confidence level for mosquito classification on offline datasets.
  • Real-time field trials demonstrated an average confidence level of 82% for mosquito detection and classification.
  • The framework successfully fused detection data onto the robot's 2D map to visualize mosquito populations.

Conclusions:

  • The AI-enabled Dragonfly robot offers an efficient and accurate solution for mosquito surveillance and population mapping.
  • The YOLO V4 DNN model shows strong performance in detecting and classifying key mosquito species.
  • This automated approach can significantly aid in understanding mosquito population dynamics and inform public health strategies.