Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908366848131072 |
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| author | Temesgen, Ebasa Jerez, Mario Brown, Greta Wilson, Graham Divakarla, Sree Ganesh Lalitaditya Boelter, Sarah Nelson, Oscar McPherson, Robert Gini, Maria |
| author_facet | Temesgen, Ebasa Jerez, Mario Brown, Greta Wilson, Graham Divakarla, Sree Ganesh Lalitaditya Boelter, Sarah Nelson, Oscar McPherson, Robert Gini, Maria |
| contents | Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for continuous operation of the UAV. In collaboration with a local Minnesota farmer, the system is tailored to address practical constraints such as terrain, infrastructure limitations, and animal behavior. The solution is evaluated through a combination of simulation and field testing, demonstrating robust detection accuracy, efficient coverage, and extended operational time. The results highlight the feasibility and effectiveness of drone-based wildlife deterrence in precision agriculture, offering a scalable framework for future deployment and extension. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD) Temesgen, Ebasa Jerez, Mario Brown, Greta Wilson, Graham Divakarla, Sree Ganesh Lalitaditya Boelter, Sarah Nelson, Oscar McPherson, Robert Gini, Maria Robotics Artificial Intelligence Multiagent Systems Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for continuous operation of the UAV. In collaboration with a local Minnesota farmer, the system is tailored to address practical constraints such as terrain, infrastructure limitations, and animal behavior. The solution is evaluated through a combination of simulation and field testing, demonstrating robust detection accuracy, efficient coverage, and extended operational time. The results highlight the feasibility and effectiveness of drone-based wildlife deterrence in precision agriculture, offering a scalable framework for future deployment and extension. |
| title | Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD) |
| topic | Robotics Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2505.10770 |