4.6 Article

Designing and Testing a UAV Mapping System for Agricultural Field Surveying

Journal

SENSORS
Volume 17, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/s17122703

Keywords

aerial robotics; canopy estimation; crop monitoring; point cloud; winter wheat mapping

Funding

  1. Intelligente VIRKemidler til reduktion af reduktion af kvaelstofudvaskningen (VIRKN) Project
  2. Danish Ministry of Environment and Foods Gront Udviklings-og Demonstrationsprogram (GUDP)
  3. FutureCropping Project - Innovation Fund Denmark [50]

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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 N/ha. 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 x 0.04 x 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.

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