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

Abstract

Drones are promising for data collection in precision agriculture but are limited by battery capacity. Drone paths are usually planned using full coverage planners, even though this is not always required. This paper presents a drone path planner trained with Reinforcement Learning (RL) to detect as many objects as possible with a minimal flight path length. The agent uses low-quality prior knowledge derived from a high-altitude full coverage flight as guidance. The agent was trained in simulation, modeling object distributions, drone movement, field geometry, detection errors, and uncertain prior knowledge. Combined with a flight controller and object-detection network, it controls flight direction, terminates flights, and can be deployed on a real drone. It was evaluated across six levels of realism, from pure simulation to real-world drone flights, to quantify the simulation-to-reality gap. The agent achieved a 57% shorter flight path than a full coverage planner in simulation (13% lower recall) and a 38% shorter flight path on real-world orthomosaic data (21% lower recall). In real-world drone flights, the agent found 73% and 23% of the objects in trials 1 and 2, respectively; the lower real-world performance was mainly attributed to prior knowledge quality. Although framed as a weed-detection task, the approach is expected to generalize to other agricultural tasks with non-uniformly distributed objects and tolerance to false negatives, though further research is needed before practical deployment.