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

TL;DR

This paper introduces a method for estimating crop heights using 3D LiDAR mounted on UAVs, enabling efficient phenotyping with high accuracy and providing a simulation toolchain and dataset for further research.

Contribution

The paper presents a novel approach for extracting plant heights from airborne 3D LiDAR data and introduces a simulation toolchain for phenotyping farm environments.

Findings

Achieved 6.1 cm RMSE in plant height estimation in field tests.

Developed a simulation toolchain for creating realistic phenotyping environments.

Provided the first dataset of 3D LiDAR data from an airborne UAV over wheat.

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.