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Conventional and hyperspectral time-series imaging of maize lines widely used in field trials
BACKGROUND: Maize (Zea mays ssp. mays) is 1 of 3 crops, along with rice and wheat, responsible for more than one-half of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenot...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795349/ https://www.ncbi.nlm.nih.gov/pubmed/29186425 http://dx.doi.org/10.1093/gigascience/gix117 |
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author | Liang, Zhikai Pandey, Piyush Stoerger, Vincent Xu, Yuhang Qiu, Yumou Ge, Yufeng Schnable, James C |
author_facet | Liang, Zhikai Pandey, Piyush Stoerger, Vincent Xu, Yuhang Qiu, Yumou Ge, Yufeng Schnable, James C |
author_sort | Liang, Zhikai |
collection | PubMed |
description | BACKGROUND: Maize (Zea mays ssp. mays) is 1 of 3 crops, along with rice and wheat, responsible for more than one-half of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping are currently the largest constraints on plant breeding efforts. Datasets linking new types of high-throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision–based tools. FINDINGS: A set of maize inbreds—primarily recently off patent lines—were phenotyped using a high-throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high-density genotyping and scored for a core set of 13 phenotypes in field trials across 13 North American states in 2 years by the Genomes 2 Fields Consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence, and thermal infrared photos has been released. CONCLUSIONS: Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors that influence yield plasticity. |
format | Online Article Text |
id | pubmed-5795349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57953492018-02-12 Conventional and hyperspectral time-series imaging of maize lines widely used in field trials Liang, Zhikai Pandey, Piyush Stoerger, Vincent Xu, Yuhang Qiu, Yumou Ge, Yufeng Schnable, James C Gigascience Data Note BACKGROUND: Maize (Zea mays ssp. mays) is 1 of 3 crops, along with rice and wheat, responsible for more than one-half of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping are currently the largest constraints on plant breeding efforts. Datasets linking new types of high-throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision–based tools. FINDINGS: A set of maize inbreds—primarily recently off patent lines—were phenotyped using a high-throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high-density genotyping and scored for a core set of 13 phenotypes in field trials across 13 North American states in 2 years by the Genomes 2 Fields Consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence, and thermal infrared photos has been released. CONCLUSIONS: Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors that influence yield plasticity. Oxford University Press 2017-11-24 /pmc/articles/PMC5795349/ /pubmed/29186425 http://dx.doi.org/10.1093/gigascience/gix117 Text en © The Authors 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Data Note Liang, Zhikai Pandey, Piyush Stoerger, Vincent Xu, Yuhang Qiu, Yumou Ge, Yufeng Schnable, James C Conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
title | Conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
title_full | Conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
title_fullStr | Conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
title_full_unstemmed | Conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
title_short | Conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
title_sort | conventional and hyperspectral time-series imaging of maize lines widely used in field trials |
topic | Data Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795349/ https://www.ncbi.nlm.nih.gov/pubmed/29186425 http://dx.doi.org/10.1093/gigascience/gix117 |
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