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A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination

BACKGROUND: Imbibition (uptake of water by a dry seed) initiates the germination process. An automated method for quantifying imbibition would enable research on the genetic elements that influence the underlying hydraulic and biochemical processes. In the case of crop research, a high throughput im...

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Autores principales: Miller, Nathan D., Stelpflug, Scott C., Kaeppler, Shawn M., Spalding, Edgar P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302439/
https://www.ncbi.nlm.nih.gov/pubmed/30598691
http://dx.doi.org/10.1186/s13007-018-0383-7
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author Miller, Nathan D.
Stelpflug, Scott C.
Kaeppler, Shawn M.
Spalding, Edgar P.
author_facet Miller, Nathan D.
Stelpflug, Scott C.
Kaeppler, Shawn M.
Spalding, Edgar P.
author_sort Miller, Nathan D.
collection PubMed
description BACKGROUND: Imbibition (uptake of water by a dry seed) initiates the germination process. An automated method for quantifying imbibition would enable research on the genetic elements that influence the underlying hydraulic and biochemical processes. In the case of crop research, a high throughput imbibition assay could be used to investigate seed quality topics or to improve yield by selecting varieties with superior germination characteristics. RESULTS: An electronic force transducer measured imbibition of single maize kernels with very high resolution but low throughput. An image analysis method was devised to achieve high throughput and sufficient resolution. A transparent fixture held 90 maize kernels in contact with water on the imaging window of a flatbed document scanner that produced an image of the kernels automatically every 10 min for 22 h. Custom image analysis software measured the area A of each indexed kernel in each image to produce imbibition time courses. The ultimate change in area (ΔA) ranged from 19.3 to 23.4% in a population of 72 hybrids derived from 9 inbred parents. Kernel area as a function of time was fit well by [Formula: see text] where A(f) is the final kernel area. The swelling coefficient, k, ranged from 0.098 to 0.159 h(−1) across the genotypes. The full diallel structure of the population enabled maternal genotype effects to be assessed. In a separate experiment, measurements of kernels of the same 25 inbreds produced in three different years demonstrated that production and storage variables affected imbibition much less than genotype. In a third experiment, measurements of 30 diverse inbred lines showed that k varied inversely with germination time (r = − 0.7) and directly with germination percentage (r = 0.7). CONCLUSIONS: Nonspecialized imaging hardware and custom analysis software running on public cyber infrastructure form a low-cost platform for measuring seed imbibition with high resolution and throughput. We measured imbibition of thousands of kernels to determine that genotype influenced imbibition of maize kernels much more than seed production and storage environments. In some hybrids, k depended on which inbred parent was maternal. Quantitative relationships between k and germination traits were discovered. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-018-0383-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-63024392018-12-31 A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination Miller, Nathan D. Stelpflug, Scott C. Kaeppler, Shawn M. Spalding, Edgar P. Plant Methods Research BACKGROUND: Imbibition (uptake of water by a dry seed) initiates the germination process. An automated method for quantifying imbibition would enable research on the genetic elements that influence the underlying hydraulic and biochemical processes. In the case of crop research, a high throughput imbibition assay could be used to investigate seed quality topics or to improve yield by selecting varieties with superior germination characteristics. RESULTS: An electronic force transducer measured imbibition of single maize kernels with very high resolution but low throughput. An image analysis method was devised to achieve high throughput and sufficient resolution. A transparent fixture held 90 maize kernels in contact with water on the imaging window of a flatbed document scanner that produced an image of the kernels automatically every 10 min for 22 h. Custom image analysis software measured the area A of each indexed kernel in each image to produce imbibition time courses. The ultimate change in area (ΔA) ranged from 19.3 to 23.4% in a population of 72 hybrids derived from 9 inbred parents. Kernel area as a function of time was fit well by [Formula: see text] where A(f) is the final kernel area. The swelling coefficient, k, ranged from 0.098 to 0.159 h(−1) across the genotypes. The full diallel structure of the population enabled maternal genotype effects to be assessed. In a separate experiment, measurements of kernels of the same 25 inbreds produced in three different years demonstrated that production and storage variables affected imbibition much less than genotype. In a third experiment, measurements of 30 diverse inbred lines showed that k varied inversely with germination time (r = − 0.7) and directly with germination percentage (r = 0.7). CONCLUSIONS: Nonspecialized imaging hardware and custom analysis software running on public cyber infrastructure form a low-cost platform for measuring seed imbibition with high resolution and throughput. We measured imbibition of thousands of kernels to determine that genotype influenced imbibition of maize kernels much more than seed production and storage environments. In some hybrids, k depended on which inbred parent was maternal. Quantitative relationships between k and germination traits were discovered. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-018-0383-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-21 /pmc/articles/PMC6302439/ /pubmed/30598691 http://dx.doi.org/10.1186/s13007-018-0383-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Miller, Nathan D.
Stelpflug, Scott C.
Kaeppler, Shawn M.
Spalding, Edgar P.
A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
title A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
title_full A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
title_fullStr A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
title_full_unstemmed A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
title_short A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
title_sort machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302439/
https://www.ncbi.nlm.nih.gov/pubmed/30598691
http://dx.doi.org/10.1186/s13007-018-0383-7
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