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 . 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.
Highlights
Aerial mapping of agricultural and forestry land provides a means to estimate current production and environmental states, and monitor progress over time
We evaluate the accuracy of mapped point clouds at estimating the structure of crop parcels, which is a significant factor that can be used as an alternative method for determining crop biomass
The results are divided into four subsections: experimental field mapping, mapping comparison, relation to treatment-plan, and crop parcel volume estimation
Summary
Aerial mapping of agricultural and forestry land provides a means to estimate current production and environmental states, and monitor progress over time. Information on production and environmental states can be used in site-specific farming to tailor specific crop and soil treatments for each field [1,2]. Low spatial resolution sensory data may underestimate productivity and environmental factors, and result in insufficient treatment coverage [3]. In [5], image data from a DJI Phantom 2 UAV [6] were used to evaluate the results of seeding an experimental field by determining unseeded rows and bare soil. Plant height has been estimated in experimental fields [7], using crop surface models derived from UAV-recorded red, green and blue (RGB) color images, and related to the extracted biomass. The relationship between barley plant height and extracted biomass was determined in [8], and developed into a prediction model for future use
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