Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current...

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Detalles Bibliográficos
Autores: Oury, V., Leroux, T., Turc, O., Chapuis, R., Palaffre, C., Tardieu, F., Alvarez Prado, Santiago, Welcker, C., Lacube, S.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/213446
Acceso en línea:http://hdl.handle.net/11336/213446
Access Level:acceso abierto
Palabra clave:CNN-BASED DEEP LEARNING
ENVIRONMENTAL RESPONSE
GRAIN ABORTION
GRAIN SET
MAIZE EAR IMAGING
MAIZE EAR SPATIAL ORGANIZATION
ZEA MAYS
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
id AR_5ac7e32df7d8d309b2d8e9da4630ca65
oai_identifier_str oai:ri.conicet.gov.ar:11336/213446
network_acronym_str AR
network_name_str Argentina
repository_id_str
dc.title.none.fl_str_mv Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
title Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
spellingShingle Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
Oury, V.
CNN-BASED DEEP LEARNING
ENVIRONMENTAL RESPONSE
GRAIN ABORTION
GRAIN SET
MAIZE EAR IMAGING
MAIZE EAR SPATIAL ORGANIZATION
ZEA MAYS
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
title_short Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
title_full Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
title_fullStr Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
title_full_unstemmed Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
title_sort Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
dc.creator.none.fl_str_mv Oury, V.
Leroux, T.
Turc, O.
Chapuis, R.
Palaffre, C.
Tardieu, F.
Alvarez Prado, Santiago
Welcker, C.
Lacube, S.
author Oury, V.
author_facet Oury, V.
Leroux, T.
Turc, O.
Chapuis, R.
Palaffre, C.
Tardieu, F.
Alvarez Prado, Santiago
Welcker, C.
Lacube, S.
author_role author
author2 Leroux, T.
Turc, O.
Chapuis, R.
Palaffre, C.
Tardieu, F.
Alvarez Prado, Santiago
Welcker, C.
Lacube, S.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv CNN-BASED DEEP LEARNING
ENVIRONMENTAL RESPONSE
GRAIN ABORTION
GRAIN SET
MAIZE EAR IMAGING
MAIZE EAR SPATIAL ORGANIZATION
ZEA MAYS
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
topic CNN-BASED DEEP LEARNING
ENVIRONMENTAL RESPONSE
GRAIN ABORTION
GRAIN SET
MAIZE EAR IMAGING
MAIZE EAR SPATIAL ORGANIZATION
ZEA MAYS
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
description Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.
publishDate 2022
dc.date.none.fl_str_mv 2022-12
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/11336/213446
Oury, V.; Leroux, T.; Turc, O.; Chapuis, R.; Palaffre, C.; et al.; Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits; BioMed Central; Plant Methods; 18; 1; 12-2022; 1-17
1746-4811
CONICET Digital
CONICET
url http://hdl.handle.net/11336/213446
identifier_str_mv Oury, V.; Leroux, T.; Turc, O.; Chapuis, R.; Palaffre, C.; et al.; Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits; BioMed Central; Plant Methods; 18; 1; 12-2022; 1-17
1746-4811
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/s13007-022-00925-8
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 BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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
_version_ 1799195492329979904
spelling Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traitsOury, V.Leroux, T.Turc, O.Chapuis, R.Palaffre, C.Tardieu, F.Alvarez Prado, SantiagoWelcker, C.Lacube, S.CNN-BASED DEEP LEARNINGENVIRONMENTAL RESPONSEGRAIN ABORTIONGRAIN SETMAIZE EAR IMAGINGMAIZE EAR SPATIAL ORGANIZATIONZEA MAYShttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.Fil: Oury, V.. No especifíca;Fil: Leroux, T.. No especifíca;Fil: Turc, O.. Université Montpellier II; FranciaFil: Chapuis, R.. Université Montpellier II; FranciaFil: Palaffre, C.. Universite de Bordeaux; FranciaFil: Tardieu, F.. Université Montpellier II; FranciaFil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Welcker, C.. Université Montpellier II; FranciaFil: Lacube, S.. No especifíca;BioMed Central2022-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213446Oury, V.; Leroux, T.; Turc, O.; Chapuis, R.; Palaffre, C.; et al.; Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits; BioMed Central; Plant Methods; 18; 1; 12-2022; 1-171746-4811CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1186/s13007-022-00925-8info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:54:13Zoai:ri.conicet.gov.ar:11336/213446instacron: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:34982024-05-08 13:54:13.86CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
score 15,812455

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