Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)

2025-05-16

AI Summary

To mitigate crop losses caused by deer intrusions in agricultural fields, this paper proposes a closed-loop autonomous drone-based deterrence system integrating geofencing, real-time YOLOv8 object detection, and energy-aware coverage path planning (CPP). The system incorporates an embedded flight controller, solar-assisted charging stations, and a ROS/Gazebo simulation–real-world co-verification framework, enabling geofence-constrained target detection, dynamic path replanning, and autonomous return-to-charging. Its key innovation lies in the first integration of an energy-aware mechanism into a farmland-adapted CPP algorithm, achieving end-to-end closed-loop operation from detection to decision-making to actuation. Experimental results demonstrate >92% deer detection accuracy, single-charge coverage of 20 acres, and a 3.1× improvement in operational endurance over baseline approaches. Full-cycle field validation has been successfully conducted on a farm in Minnesota.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Addressing deer-induced crop damage in agriculture
• Overcoming limitations of traditional wildlife deterrence methods
• Developing autonomous UAV system for efficient deer detection and deterrence

Innovation

• YOLO-based real-time deer detection module
• Energy-efficient coverage path planning algorithm
• Autonomous charging station for UAV