Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods

Abstract

Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50-100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.

Plant phenotyping relevance

SfMと携帯型LiDARによる牧草高の推定手法を開発・比較し、点群からDSMへの変換方法と測定精度を評価しており、植物形質取得が研究の中心です。

Code and data availability

The article describes LiDAR/SfM grass height measurements but contains no data availability statement, no public deposit of point clouds, images, or phenotype data, and no author analysis code or URL. The only URLs present are the CC BY license, the R project site (generic library), and a cited prior-work reference (KU

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