Presentation
30 May 2022 Phenotyping of field crops using 3D LiDAR point cloud and RGB imaging system
Author Affiliations +
Abstract
It is crucial to improve the efficiency of plant breeding and crop yield in order to fulfill rising food demands. In plant phenotyping study, the capability to correlate morphological traits of plants plays an important role. However, measuring the plant phenotypes manually is prone to human errors, labor intensive and time-consuming. Hence, it is important to develop techniques for measurement of plant phenotypic data accurately and rapidly. The objective of this study was to find out the feasibility of point cloud data of 3D LiDAR including RGB image for plant phenotyping. The obtained results were then verified through the manually acquired data for sorghum and soybean plant samples. The overall results showed remarkable correlation between point cloud data and manually acquired data for plant phenotyping. This correlation indicates that the 3D Lidar imaging system have potential to measure phenotypes of crops in rapid and accurate way.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eun-sung Park, Ajay P. Kumar, and Byoung-Kwan Cho "Phenotyping of field crops using 3D LiDAR point cloud and RGB imaging system", Proc. SPIE PC12120, Sensing for Agriculture and Food Quality and Safety XIV, PC121200C (30 May 2022); https://doi.org/10.1117/12.2621150
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KEYWORDS
3D image processing

Clouds

LIDAR

Imaging systems

RGB color model

Data acquisition

3D metrology

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