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Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat
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 analyze big data collected. Here, we present a large-scal...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491082/ https://www.ncbi.nlm.nih.gov/pubmed/34608970 http://dx.doi.org/10.1093/plphys/kiab324 |
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author | Zhu, Yulei Sun, Gang Ding, Guohui Zhou, Jie Wen, Mingxing Jin, Shichao Zhao, Qiang Colmer, Joshua Ding, Yanfeng Ober, Eric S. Zhou, Ji |
author_facet | Zhu, Yulei Sun, Gang Ding, Guohui Zhou, Jie Wen, Mingxing Jin, Shichao Zhao, Qiang Colmer, Joshua Ding, Yanfeng Ober, Eric S. Zhou, Ji |
author_sort | Zhu, Yulei |
collection | PubMed |
description | 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 analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (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 fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the 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. |
format | Online Article Text |
id | pubmed-8491082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84910822021-10-06 Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat Zhu, Yulei Sun, Gang Ding, Guohui Zhou, Jie Wen, Mingxing Jin, Shichao Zhao, Qiang Colmer, Joshua Ding, Yanfeng Ober, Eric S. Zhou, Ji Plant Physiol Regular Issue 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 analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (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 fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the 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. Oxford University Press 2021-07-16 /pmc/articles/PMC8491082/ /pubmed/34608970 http://dx.doi.org/10.1093/plphys/kiab324 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Regular Issue Zhu, Yulei Sun, Gang Ding, Guohui Zhou, Jie Wen, Mingxing Jin, Shichao Zhao, Qiang Colmer, Joshua Ding, Yanfeng Ober, Eric S. Zhou, Ji Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat |
title | Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat |
title_full | Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat |
title_fullStr | Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat |
title_full_unstemmed | Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat |
title_short | Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat |
title_sort | large-scale field phenotyping using backpack lidar and cropquant-3d to measure structural variation in wheat |
topic | Regular Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491082/ https://www.ncbi.nlm.nih.gov/pubmed/34608970 http://dx.doi.org/10.1093/plphys/kiab324 |
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