Evaluating Path Planning Strategies for Efficient Nitrate Sampling in Crop Rows

Abstract: This paper presents a pipeline that combines high-resolution orthomosaic maps generated from UAS imagery with GPS-based global navigation to guide a skid-steered ground robot. We evaluated three path planning strategies: A* Graph search, Deep Q-learning (DQN) model, and Heuristic search, benchmarking them on planning time and success rate in realistic simulation environments. Experimental results reveal that the Heuristic search achieves the fastest planning times (0.28 ms) and a 100% success rate, while the A* approach delivers nearoptimal performance, and the DQN model, despite its adaptability, incurs longer planning delays and occasional suboptimal routing. These results highlight the advantages of deterministic rulebased methods in geometrically constrained crop-row environments and lay the groundwork for future hybrid strategies in precision agriculture. Keywords: Path planning, autonomous control, crop rows, autonomous nitrate sampling 1. INTRODUCTION Autonomous navigation in agricultural fields is challenging due to structured layouts with unstructured variability.