Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR
This work addresses high-throughput plant phenotyping for agricultural monitoring, but it is incremental as it applies existing LiDAR methods to a new domain with specific tools and datasets.
The paper tackled the problem of measuring crop heights for plant phenotyping using 3D LiDAR on UAVs, achieving a root mean square error of 6.1 cm in estimating heights across 112 plots in a wheat field.
We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.