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Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets

BACKGROUND: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique...

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Autores principales: Heeren, Behrend, Paulus, Stefan, Goldbach, Heiner, Kuhlmann, Heiner, Mahlein, Anne-Katrin, Rumpf, Martin, Wirth, Benedikt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388232/
https://www.ncbi.nlm.nih.gov/pubmed/32727350
http://dx.doi.org/10.1186/s12859-020-03654-8
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author Heeren, Behrend
Paulus, Stefan
Goldbach, Heiner
Kuhlmann, Heiner
Mahlein, Anne-Katrin
Rumpf, Martin
Wirth, Benedikt
author_facet Heeren, Behrend
Paulus, Stefan
Goldbach, Heiner
Kuhlmann, Heiner
Mahlein, Anne-Katrin
Rumpf, Martin
Wirth, Benedikt
author_sort Heeren, Behrend
collection PubMed
description BACKGROUND: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination. RESULTS: The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results. CONCLUSION: The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.
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spelling pubmed-73882322020-07-31 Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets Heeren, Behrend Paulus, Stefan Goldbach, Heiner Kuhlmann, Heiner Mahlein, Anne-Katrin Rumpf, Martin Wirth, Benedikt BMC Bioinformatics Research Article BACKGROUND: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination. RESULTS: The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results. CONCLUSION: The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable. BioMed Central 2020-07-29 /pmc/articles/PMC7388232/ /pubmed/32727350 http://dx.doi.org/10.1186/s12859-020-03654-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Heeren, Behrend
Paulus, Stefan
Goldbach, Heiner
Kuhlmann, Heiner
Mahlein, Anne-Katrin
Rumpf, Martin
Wirth, Benedikt
Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
title Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
title_full Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
title_fullStr Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
title_full_unstemmed Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
title_short Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
title_sort statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388232/
https://www.ncbi.nlm.nih.gov/pubmed/32727350
http://dx.doi.org/10.1186/s12859-020-03654-8
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