A drone that learns to efficiently find objects in agricultural fields: from simulation to the real world

TL;DR

This paper introduces a reinforcement learning-based drone path planner trained in simulation that efficiently reduces flight path length in agricultural data collection, with promising transfer to real-world scenarios despite some performance trade-offs.

Contribution

It presents a novel RL-based path planning approach trained in simulation and successfully applied to real-world agricultural drone data, demonstrating significant efficiency gains.

Findings

78% shorter flight path in simulation

72% shorter flight path in real-world data

Potential for improvement in real-world object detection

Abstract

Drones are promising for data collection in precision agriculture, however, they are limited by their battery capacity. Efficient path planners are therefore required. This paper presents a drone path planner trained using Reinforcement Learning (RL) on an abstract simulation that uses object detections and uncertain prior knowledge. The RL agent controls the flight direction and can terminate the flight. By using the agent in combination with the drone's flight controller and a detection network to process camera images, it is possible to evaluate the performance of the agent on real-world data. In simulation, the agent yielded on average a 78% shorter flight path compared to a full coverage planner, at the cost of a 14% lower recall. On real-world data, the agent showed a 72% shorter flight path compared to a full coverage planner, however, at the cost of a 25% lower recall. The lower performance on real-world data was attributed to the real-world object distribution and the lower accuracy of prior knowledge, and shows potential for improvement. Overall, we concluded that for applications where it is not crucial to find all objects, such as weed detection, the learned-based path planner is suitable and efficient.