Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat:Portable LiDAR and 3D field phenotyping in wheat

Zhu, Yulei, Sun, Gang, Ding, Guohui, Zhou, Jie, Wen, Mingxing, Jin, Shichao, Zhao, Qiang, Colmer, Joshua, Ding, Yanfeng, Ober, Eric S and Zhou, Ji (2021) Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat:Portable LiDAR and 3D field phenotyping in wheat. Plant Physiology. ISSN 0032-0889

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Abstract

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability and the ability to analyse big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack LiDAR device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilisation in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We therefore believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.

Item Type: Article
Uncontrolled Keywords: breakthrough technologies,tools & resources,wheat,lidar,3d image analysis,machine learning
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 23 Jul 2021 00:20
Last Modified: 06 Sep 2021 00:07
URI: https://ueaeprints.uea.ac.uk/id/eprint/80720
DOI: 10.1093/plphys/kiab324

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