Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR

Abstract

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.

Plant phenotyping relevance

UAV搭載3D LiDARによる作物高の抽出手法、シミュレーション用ツールチェーン、検証実験、データセットを中心に扱っており、植物表現型取得法が明確に中心である。

Code and data availability

The authors explicitly release their point cloud processing tools, real-world wheat LiDAR datasets, and simulation farm-generation toolchain on their public GitHub repository. The Turbosquid URL only references the license for commercial third-party soybean 3D models, not a paper-specific asset.