Noncontact measurements of the morphological phenotypes of sorghum using 3D LiDAR point cloud

Abstract

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands.In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role.However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming.Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly.The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping.The obtained results were then verified through manually acquired data from the sorghum samples.This study measured the plant height, plant crown diameter and the panicle height and diameter.The R 2 of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively.The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping.The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.

Plant phenotyping relevance

3D LiDARによるソルガム形態形質の非接触取得法を開発・検証しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The article describes 3D LiDAR point cloud phenotyping of sorghum (plant height, crown diameter, panicle length/diameter) but contains no data availability statement, no public dataset or point cloud deposit, and no author code release. Only generic tools (Open3D, CloudCompare, OpenCV) are mentioned, which are not this

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