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A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

BACKGROUND: In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is dif...

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Autores principales: Brichet, Nicolas, Fournier, Christian, Turc, Olivier, Strauss, Olivier, Artzet, Simon, Pradal, Christophe, Welcker, Claude, Tardieu, François, Cabrera-Bosquet, Llorenç
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688816/
https://www.ncbi.nlm.nih.gov/pubmed/29176999
http://dx.doi.org/10.1186/s13007-017-0246-7
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author Brichet, Nicolas
Fournier, Christian
Turc, Olivier
Strauss, Olivier
Artzet, Simon
Pradal, Christophe
Welcker, Claude
Tardieu, François
Cabrera-Bosquet, Llorenç
author_facet Brichet, Nicolas
Fournier, Christian
Turc, Olivier
Strauss, Olivier
Artzet, Simon
Pradal, Christophe
Welcker, Claude
Tardieu, François
Cabrera-Bosquet, Llorenç
author_sort Brichet, Nicolas
collection PubMed
description BACKGROUND: In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. RESULTS: We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. CONCLUSIONS: The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-017-0246-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-56888162017-11-24 A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform Brichet, Nicolas Fournier, Christian Turc, Olivier Strauss, Olivier Artzet, Simon Pradal, Christophe Welcker, Claude Tardieu, François Cabrera-Bosquet, Llorenç Plant Methods Methodology BACKGROUND: In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. RESULTS: We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. CONCLUSIONS: The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-017-0246-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-08 /pmc/articles/PMC5688816/ /pubmed/29176999 http://dx.doi.org/10.1186/s13007-017-0246-7 Text en © The Author(s) 2017 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 Methodology
Brichet, Nicolas
Fournier, Christian
Turc, Olivier
Strauss, Olivier
Artzet, Simon
Pradal, Christophe
Welcker, Claude
Tardieu, François
Cabrera-Bosquet, Llorenç
A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
title A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
title_full A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
title_fullStr A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
title_full_unstemmed A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
title_short A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
title_sort robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688816/
https://www.ncbi.nlm.nih.gov/pubmed/29176999
http://dx.doi.org/10.1186/s13007-017-0246-7
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