Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline

Lionel Xavier Dupuy; Gladys Wright; Jacqueline A. Thompson; Anna Taylor; Sebastien Dekeyser; Christopher P. White; William T. B. Thomas; Mark Nightingale; John P. Hammond; Neil S. Graham; Catherine L. Thomas; Martin R. Broadley; Philip J. White

doi:10.1186/s13007-017-0207-1

Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline

Lionel Xavier Dupuy (Lead / Corresponding author), Gladys Wright, Jacqueline A. Thompson, Anna Taylor, Sebastien Dekeyser, Christopher P. White, William T. B. Thomas, Mark Nightingale, John P. Hammond, Neil S. Graham, Catherine L. Thomas, Martin R. Broadley, Philip J. White

Plant Sciences

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11 Citations (Scopus)

257 Downloads (Pure)

Abstract

Background: There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.

Results: Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database.

Conclusions: Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

Original language	English
Article number	57
Pages (from-to)	1-14
Number of pages	14
Journal	Plant Methods
Volume	13
DOIs	https://doi.org/10.1186/s13007-017-0207-1
Publication status	Published - 13 Jul 2017

Keywords

Root
Phenotyping
Error
Pipeline
Barley
Brassica

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10.1186/s13007-017-0207-1Licence: CC BY

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© The Author(s) 2017. This 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.
Final published version, 3.01 MBLicence: CC BY

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Dupuy, L. X., Wright, G., Thompson, J. A., Taylor, A., Dekeyser, S., White, C. P., Thomas, W. T. B., Nightingale, M., Hammond, J. P., Graham, N. S., Thomas, C. L., Broadley, M. R., & White, P. J. (2017). Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline. Plant Methods, 13, 1-14. Article 57. https://doi.org/10.1186/s13007-017-0207-1

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title = "Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline",

abstract = "Background: There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.Results: Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database.Conclusions: Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.",

keywords = "Root, Phenotyping, Error, Pipeline, Barley, Brassica",

author = "Dupuy, {Lionel Xavier} and Gladys Wright and Thompson, {Jacqueline A.} and Anna Taylor and Sebastien Dekeyser and White, {Christopher P.} and Thomas, {William T. B.} and Mark Nightingale and Hammond, {John P.} and Graham, {Neil S.} and Thomas, {Catherine L.} and Broadley, {Martin R.} and White, {Philip J.}",

note = "The work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Crop Improvement Research Club (CIRC) Grant BB/J019631/1, and the Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government through Work Package 2.1.7 (2016–2021).",

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AU - Dupuy, Lionel Xavier

AU - Wright, Gladys

AU - Thompson, Jacqueline A.

AU - Taylor, Anna

AU - Dekeyser, Sebastien

AU - White, Christopher P.

AU - Thomas, William T. B.

AU - Nightingale, Mark

AU - Hammond, John P.

AU - Graham, Neil S.

AU - Thomas, Catherine L.

AU - Broadley, Martin R.

AU - White, Philip J.

N1 - The work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Crop Improvement Research Club (CIRC) Grant BB/J019631/1, and the Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government through Work Package 2.1.7 (2016–2021).

PY - 2017/7/13

Y1 - 2017/7/13

N2 - Background: There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.Results: Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database.Conclusions: Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

AB - Background: There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.Results: Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database.Conclusions: Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

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KW - Error

KW - Pipeline

KW - Barley

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DO - 10.1186/s13007-017-0207-1

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SP - 1

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JO - Plant Methods

JF - Plant Methods

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ER -

Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline

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Keywords

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