Phenotyping of field crops using 3D LiDAR point cloud and RGB imaging system

Eun-sung Park; Ajay P. Kumar; Byoung-Kwan Cho

doi:10.1117/12.2621150

30 May 2022 Phenotyping of field crops using 3D LiDAR point cloud and RGB imaging system

Eun-sung Park, Ajay P. Kumar, Byoung-Kwan Cho

Proceedings Volume PC12120, Sensing for Agriculture and Food Quality and Safety XIV; PC121200C (2022) https://doi.org/10.1117/12.2621150
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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

Citation Download Citation

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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