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

TL;DR

This paper introduces an autonomous UAV system that detects and deters deer in farmland using real-time computer vision, efficient path planning, and autonomous charging, demonstrating its effectiveness through simulation and field tests.

Contribution

The paper presents a novel integrated UAV system combining real-time deer detection, energy-efficient coverage, and autonomous charging tailored for agricultural wildlife deterrence.

Findings

Robust deer detection accuracy in field conditions

Efficient field coverage with optimized path planning

Extended UAV operational time through autonomous charging

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.