Accurate agricultural weed mapping using unmanned aerial vehicles (UAVs) is crucial for precision farming. While traditional methods rely on rigid, pre-defined flight paths and intensive offline processing, informative path planning (IPP) offers a way to collect data adaptively where it is most needed. Gaussian process (GP) mapping provides a continuous model of weed distribution with built-in uncertainty. However, GPs must be discretised for practical use in autonomous planning. Many discretisation techniques exist, but the impact of discrete representation choice remains poorly understood. This paper investigates how different discrete GP representations influence both mapping quality and mission-level performance in UAV-based weed mapping. Considering a UAV equipped with a downward-facing camera, we implement a receding-horizon IPP strategy that selects sampling locations based on the map uncertainty, travel cost, and coverage penalties. We investigate multiple discretisation strategies for representing the GP posterior and use their induced map partitions to generate candidate viewpoints for planning. Experiments on real-world weed distributions show that representation choice significantly affects exploration behaviour and efficiency. Overall, our results demonstrate that discretisation is not only a representational detail but a key design choice that shapes planning dynamics, coverage efficiency, and computational load in online UAV weed mapping.

翻译：利用无人机进行精准农业杂草测绘对精细农业至关重要。传统方法依赖刚性预定义飞行路径和密集离线处理，而信息路径规划（IPP）提供了一种在最需要区域自适应收集数据的方式。高斯过程（GP）建图通过内置不确定性提供了杂草分布的连续模型。然而，在实际自主规划应用中，GP必须进行离散化处理。现有多种离散化技术，但离散表示选择的影响仍未得到充分理解。本文研究了不同离散GP表示如何影响无人机杂草测绘中的建图质量和任务级性能。针对搭载下视相机的无人机，我们实现了基于地图不确定性、行进成本和覆盖惩罚的滚动时域IPP策略来选择采样位置。我们研究了多种用于表示GP后验的离散化策略，并利用其导出的地图分区生成规划候选视点。在真实杂草分布上的实验表明，表示选择显著影响探索行为和效率。总体而言，我们的结果表明离散化不仅是表示细节，更是影响在线无人机杂草测绘中规划动态、覆盖效率和计算负载的关键设计选择。