Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

View ORCID ProfileZhikai Liang, Piyush Pandey, Vincent Stoerger, Yuhang Xu, Yumou Qiu, Yufeng Ge, View ORCID ProfileJames C. Schnable
doi: https://doi.org/10.1101/169045
Zhikai Liang
1Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zhikai Liang
Piyush Pandey
2Department of Biological System Engineering, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vincent Stoerger
3Plant Phenotyping Facilities Manager, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yuhang Xu
4Department of Statistics, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yumou Qiu
4Department of Statistics, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yufeng Ge
2Department of Biological System Engineering, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
James C. Schnable
1Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, 68503, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for James C. Schnable
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 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 is currently the largest constraint 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. 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 two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. 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 influencing yield plasticity.

Footnotes

  • ↵* schnable{at}unl.edu

  • Competing Interests: The authors declare that they have no competing interests.

  • Declarations

    DAP
    Days after planting
    GBS
    Genotyping by Sequencing
    LED
    Light-emitting diode
    NDVI
    Normalized difference vegetation index
    NIR
    Near-infrared
    RGB
    Red, Blue and Green
    SNP
    Single Nucleotide Polymorphism
    UNL
    University of Nebraska-Lincoln
    PA0
    Plant Area calculated from a 0 degree image
    PA90
    Plant Area calculated from a 90 degree image
    PCA
    Principal Component Analysis
    PH0
    Plant Height calculated from a 0 degree image
    PH90
    Plant Height calculated from a 90 degree image
    PW0
    Plant Width calculated from a 0 degree image
    PW90
    Plant Width calculated from a 90 degree image
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
    Back to top
    PreviousNext
    Posted July 28, 2017.
    Download PDF

    Supplementary Material

    Email

    Thank you for your interest in spreading the word about bioRxiv.

    NOTE: Your email address is requested solely to identify you as the sender of this article.

    Enter multiple addresses on separate lines or separate them with commas.
    Conventional and hyperspectral time-series imaging of maize lines widely used in field trials
    (Your Name) has forwarded a page to you from bioRxiv
    (Your Name) thought you would like to see this page from the bioRxiv website.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Share
    Conventional and hyperspectral time-series imaging of maize lines widely used in field trials
    Zhikai Liang, Piyush Pandey, Vincent Stoerger, Yuhang Xu, Yumou Qiu, Yufeng Ge, James C. Schnable
    bioRxiv 169045; doi: https://doi.org/10.1101/169045
    Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
    Citation Tools
    Conventional and hyperspectral time-series imaging of maize lines widely used in field trials
    Zhikai Liang, Piyush Pandey, Vincent Stoerger, Yuhang Xu, Yumou Qiu, Yufeng Ge, James C. Schnable
    bioRxiv 169045; doi: https://doi.org/10.1101/169045

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Subject Area

    • Plant Biology
    Subject Areas
    All Articles
    • Animal Behavior and Cognition (3686)
    • Biochemistry (7766)
    • Bioengineering (5666)
    • Bioinformatics (21234)
    • Biophysics (10552)
    • Cancer Biology (8157)
    • Cell Biology (11902)
    • Clinical Trials (138)
    • Developmental Biology (6736)
    • Ecology (10387)
    • Epidemiology (2065)
    • Evolutionary Biology (13838)
    • Genetics (9693)
    • Genomics (13054)
    • Immunology (8120)
    • Microbiology (19932)
    • Molecular Biology (7824)
    • Neuroscience (42955)
    • Paleontology (318)
    • Pathology (1276)
    • Pharmacology and Toxicology (2256)
    • Physiology (3350)
    • Plant Biology (7207)
    • Scientific Communication and Education (1309)
    • Synthetic Biology (1998)
    • Systems Biology (5528)
    • Zoology (1126)