Active Informative Planning for UAV-based Weed Mapping using Discrete Gaussian Process Representations

TL;DR

This paper explores how different discretisation strategies for Gaussian process models impact the effectiveness and efficiency of UAV-based weed mapping, emphasizing the importance of representation choices in planning and mapping quality.

Contribution

It systematically analyzes the influence of discrete GP representations on UAV weed mapping performance, highlighting discretisation as a crucial design factor.

Findings

Representation choice affects exploration and coverage efficiency.

Discretisation impacts computational load and planning dynamics.

Key design choice for online UAV weed mapping.

Abstract

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.