ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Autores
Gaggion Zulpo, Rafael Nicolás; Ariel, Federico Damian; Daric, Vladimir; Lambert, Éric; Legendre, Simon; Roulé, Thomas; Camoirano, Alejandra; Milone, Diego Humberto; Crespi, Martin; Blein, Thomas; Ferrante, Enzo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. Results: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Conclusions: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Fil: Gaggion Zulpo, Rafael Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Ariel, Federico Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina
Fil: Daric, Vladimir. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Lambert, Éric. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Legendre, Simon. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Roulé, Thomas. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Camoirano, Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Crespi, Martin. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Blein, Thomas. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Materia
3D-PRINTED HARDWARE
CONVOLUTIONAL NEURAL NETWORKS
IMAGE SEGMENTATION
ROOT SYSTEM ARCHITECTURE
TEMPORAL PHENOTYPING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/182752
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/182752
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architectureGaggion Zulpo, Rafael NicolásAriel, Federico DamianDaric, VladimirLambert, ÉricLegendre, SimonRoulé, ThomasCamoirano, AlejandraMilone, Diego HumbertoCrespi, MartinBlein, ThomasFerrante, Enzo3D-PRINTED HARDWARECONVOLUTIONAL NEURAL NETWORKSIMAGE SEGMENTATIONROOT SYSTEM ARCHITECTURETEMPORAL PHENOTYPINGhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Background: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. Results: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Conclusions: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.Fil: Gaggion Zulpo, Rafael Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Ariel, Federico Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Daric, Vladimir. University Paris-Saclay; Francia. University of Paris Bâtiment; FranciaFil: Lambert, Éric. University Paris-Saclay; Francia. University of Paris Bâtiment; FranciaFil: Legendre, Simon. University Paris-Saclay; Francia. University of Paris Bâtiment; FranciaFil: Roulé, Thomas. University Paris-Saclay; Francia. University of Paris Bâtiment; FranciaFil: Camoirano, Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Crespi, Martin. University Paris-Saclay; Francia. University of Paris Bâtiment; FranciaFil: Blein, Thomas. University Paris-Saclay; Francia. University of Paris Bâtiment; FranciaFil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaOxford Academic2021-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/182752Gaggion Zulpo, Rafael Nicolás; Ariel, Federico Damian; Daric, Vladimir; Lambert, Éric; Legendre, Simon; et al.; ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture; Oxford Academic; GigaScience; 10; 7; 7-2021; 1-152047-217XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/gigascience/article/10/7/giab052/6324285info:eu-repo/semantics/altIdentifier/doi/10.1093/gigascience/giab052info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:29:53Zoai:ri.conicet.gov.ar:11336/182752instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:29:54.218CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
title ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
spellingShingle ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
Gaggion Zulpo, Rafael Nicolás
3D-PRINTED HARDWARE
CONVOLUTIONAL NEURAL NETWORKS
IMAGE SEGMENTATION
ROOT SYSTEM ARCHITECTURE
TEMPORAL PHENOTYPING
title_short ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
title_full ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
title_fullStr ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
title_full_unstemmed ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
title_sort ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture
dc.creator.none.fl_str_mv Gaggion Zulpo, Rafael Nicolás
Ariel, Federico Damian
Daric, Vladimir
Lambert, Éric
Legendre, Simon
Roulé, Thomas
Camoirano, Alejandra
Milone, Diego Humberto
Crespi, Martin
Blein, Thomas
Ferrante, Enzo
author Gaggion Zulpo, Rafael Nicolás
author_facet Gaggion Zulpo, Rafael Nicolás
Ariel, Federico Damian
Daric, Vladimir
Lambert, Éric
Legendre, Simon
Roulé, Thomas
Camoirano, Alejandra
Milone, Diego Humberto
Crespi, Martin
Blein, Thomas
Ferrante, Enzo
author_role author
author2 Ariel, Federico Damian
Daric, Vladimir
Lambert, Éric
Legendre, Simon
Roulé, Thomas
Camoirano, Alejandra
Milone, Diego Humberto
Crespi, Martin
Blein, Thomas
Ferrante, Enzo
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv 3D-PRINTED HARDWARE
CONVOLUTIONAL NEURAL NETWORKS
IMAGE SEGMENTATION
ROOT SYSTEM ARCHITECTURE
TEMPORAL PHENOTYPING
topic 3D-PRINTED HARDWARE
CONVOLUTIONAL NEURAL NETWORKS
IMAGE SEGMENTATION
ROOT SYSTEM ARCHITECTURE
TEMPORAL PHENOTYPING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Background: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. Results: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Conclusions: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Fil: Gaggion Zulpo, Rafael Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Ariel, Federico Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina
Fil: Daric, Vladimir. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Lambert, Éric. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Legendre, Simon. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Roulé, Thomas. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Camoirano, Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Crespi, Martin. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Blein, Thomas. University Paris-Saclay; Francia. University of Paris Bâtiment; Francia
Fil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
description Background: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. Results: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Conclusions: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
publishDate 2021
dc.date.none.fl_str_mv 2021-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/182752
Gaggion Zulpo, Rafael Nicolás; Ariel, Federico Damian; Daric, Vladimir; Lambert, Éric; Legendre, Simon; et al.; ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture; Oxford Academic; GigaScience; 10; 7; 7-2021; 1-15
2047-217X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/182752
identifier_str_mv Gaggion Zulpo, Rafael Nicolás; Ariel, Federico Damian; Daric, Vladimir; Lambert, Éric; Legendre, Simon; et al.; ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture; Oxford Academic; GigaScience; 10; 7; 7-2021; 1-15
2047-217X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/gigascience/article/10/7/giab052/6324285
info:eu-repo/semantics/altIdentifier/doi/10.1093/gigascience/giab052
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford Academic
publisher.none.fl_str_mv Oxford Academic
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.216834

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