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High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation

Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phe...

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Autores principales: Rist, Florian, Herzog, Katja, Mack, Jenny, Richter, Robert, Steinhage, Volker, Töpfer, Reinhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876746/
https://www.ncbi.nlm.nih.gov/pubmed/29498702
http://dx.doi.org/10.3390/s18030763
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author Rist, Florian
Herzog, Katja
Mack, Jenny
Richter, Robert
Steinhage, Volker
Töpfer, Reinhard
author_facet Rist, Florian
Herzog, Katja
Mack, Jenny
Richter, Robert
Steinhage, Volker
Töpfer, Reinhard
author_sort Rist, Florian
collection PubMed
description Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phenotyping labor-intensive and time-consuming. In the present study, a fast and high-precision phenotyping pipeline was developed. The optical sensor Artec Spider 3D scanner (Artec 3D, L-1466, Luxembourg) was used to generate dense 3D point clouds of grapevine bunches under lab conditions and an automated analysis software called 3D-Bunch-Tool was developed to extract different single 3D bunch traits, i.e., the number of berries, berry diameter, single berry volume, total volume of berries, convex hull volume of grapes, bunch width and bunch length. The method was validated on whole bunches of different grapevine cultivars and phenotypic variable breeding material. Reliable phenotypic data were obtained which show high significant correlations (up to r(2) = 0.95 for berry number) compared to ground truth data. Moreover, it was shown that the Artec Spider can be used directly in the field where achieved data show comparable precision with regard to the lab application. This non-invasive and non-contact field application facilitates the first high-precision phenotyping pipeline based on 3D bunch traits in large plant sets.
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spelling pubmed-58767462018-04-09 High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation Rist, Florian Herzog, Katja Mack, Jenny Richter, Robert Steinhage, Volker Töpfer, Reinhard Sensors (Basel) Article Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phenotyping labor-intensive and time-consuming. In the present study, a fast and high-precision phenotyping pipeline was developed. The optical sensor Artec Spider 3D scanner (Artec 3D, L-1466, Luxembourg) was used to generate dense 3D point clouds of grapevine bunches under lab conditions and an automated analysis software called 3D-Bunch-Tool was developed to extract different single 3D bunch traits, i.e., the number of berries, berry diameter, single berry volume, total volume of berries, convex hull volume of grapes, bunch width and bunch length. The method was validated on whole bunches of different grapevine cultivars and phenotypic variable breeding material. Reliable phenotypic data were obtained which show high significant correlations (up to r(2) = 0.95 for berry number) compared to ground truth data. Moreover, it was shown that the Artec Spider can be used directly in the field where achieved data show comparable precision with regard to the lab application. This non-invasive and non-contact field application facilitates the first high-precision phenotyping pipeline based on 3D bunch traits in large plant sets. MDPI 2018-03-02 /pmc/articles/PMC5876746/ /pubmed/29498702 http://dx.doi.org/10.3390/s18030763 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rist, Florian
Herzog, Katja
Mack, Jenny
Richter, Robert
Steinhage, Volker
Töpfer, Reinhard
High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation
title High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation
title_full High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation
title_fullStr High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation
title_full_unstemmed High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation
title_short High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation
title_sort high-precision phenotyping of grape bunch architecture using fast 3d sensor and automation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876746/
https://www.ncbi.nlm.nih.gov/pubmed/29498702
http://dx.doi.org/10.3390/s18030763
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