Designing and Testing a UAV Mapping System for Agricultural Field Surveying

TLDR

A LiDAR sensor on a UAV can generate point clouds of the overflown area, enabling canopy height mapping that supports crop biomass estimation. The study aims to design a UAV‑based sensory system for mapping and analyzing agricultural fields, demonstrated through LiDAR recordings in a winter wheat experiment. LiDAR data are fused with GNSS and IMU readings, processed in ROS and PCL, and used to generate voxel‑grid point clouds at 0.04 × 0.04 × 0.001 m resolution, with two 6‑m flight patterns tested to evaluate volume estimation. Crop height estimates of 0.35–0.58 m were found to correlate with nitrogen treatments ranging from 0 to 300 kg.

Abstract

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35-0.58 m are correlated to the applied nitrogen treatments of 0-300 kg (Formula Presented). The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.