Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

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Publication Details

Journal
Applied Sciences
Published
2024-08-21
DOI
https://doi.org/10.3390/app14167382
Citations
4
Primary Topic
Fire Detection and Safety Systems
Type
article
Field-Weighted Citation Impact
1.46

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article

Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics

Mohan Rajesh Elara, Prithvi Krishna Chittoor, C. S. Vikram, Sidharth Jeyabal
4 citations
Applied Sciences
Fire Detection and Safety Systems
1.46
article

Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics

Mohan Rajesh Elara, Prithvi Krishna Chittoor, C. S. Vikram, Sidharth Jeyabal
article en
4 citations

Abstract

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

Applied SciencesVol. 14(16)
Singapore University of Technology and Design (SG)
Singapore University of Technology and Design
Sustainable cities and communities
Openalex Percentile: Top 19%
Fire Detection and Safety Systems
1.46
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Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics — Mohan Rajesh Elara, Prithvi Krishna Chittoor, et al. · Applied Sciences (2024) | TGRS Research Map | TGRS