UAV and ground image-based phenotyping: A proof of concept with durum wheat

25 Pág.

Detalles Bibliográficos
Autores: Gracia-Romero, Adrián, Kefauver, Shawn C., Fernández-Gallego, José A., Vergara-Díaz, Omar, Nieto-Taladriz, María Teresa, Araus, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/365962
Acceso en línea:http://hdl.handle.net/10261/365962
https://api.elsevier.com/content/abstract/scopus_id/85066740556
Access Level:acceso abierto
Palabra clave:Canopy temperature
Grain yield
High-Throughput Plant Phenotyping
Multispectral
RGB
UAV
Wheat
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spelling UAV and ground image-based phenotyping: A proof of concept with durum wheatGracia-Romero, AdriánKefauver, Shawn C.Fernández-Gallego, José A.Vergara-Díaz, OmarNieto-Taladriz, María TeresaAraus, José LuisCanopy temperatureGrain yieldHigh-Throughput Plant PhenotypingMultispectralRGBUAVWheat25 Pág.Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red-green-blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn't any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.This study was supported by the Spanish project AGL2016-76527-R “Fenotipeado En Trigo Duro: Bases Fisiológicas, Criterios De Selección Y Plataformas De Evaluación”, from the Ministerio Economía y Competitividad of the Spanish Government. A.G.-R. is a recipient of a FPI doctoral fellowship from the same institution. We also acknowledge the support from the Institut de Recerca de l’Aigua and the Universitat de Barcelona. J.L.A. acknowledges the funding support from ICREA, Generalitat de Catalunya, Spain.Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Economía y Competitividad (España)Universidad de BarcelonaGeneralitat de CatalunyaGracia-Romero, Adrián [0000-0001-8308-9693]Kefauver, Shawn C. [0000-0002-1687-1965]Fernandez-Gallego, J. A. [0000-0001-8928-4801]Vergara-Díaz, Omar [0000-0001-7074-0774]Nieto-Taladriz, María Teresa [0000-0001-6119-4249]Araus, José Luis [0000-0002-8866-2388]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/365962https://api.elsevier.com/content/abstract/scopus_id/85066740556reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO//AGL2016-76527-RDepartamento de Protección Vegetalhttps://doi.org/10.3390/rs11101244Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3659622026-05-22T06:33:51Z
dc.title.none.fl_str_mv UAV and ground image-based phenotyping: A proof of concept with durum wheat
title UAV and ground image-based phenotyping: A proof of concept with durum wheat
spellingShingle UAV and ground image-based phenotyping: A proof of concept with durum wheat
Gracia-Romero, Adrián
Canopy temperature
Grain yield
High-Throughput Plant Phenotyping
Multispectral
RGB
UAV
Wheat
title_short UAV and ground image-based phenotyping: A proof of concept with durum wheat
title_full UAV and ground image-based phenotyping: A proof of concept with durum wheat
title_fullStr UAV and ground image-based phenotyping: A proof of concept with durum wheat
title_full_unstemmed UAV and ground image-based phenotyping: A proof of concept with durum wheat
title_sort UAV and ground image-based phenotyping: A proof of concept with durum wheat
dc.creator.none.fl_str_mv Gracia-Romero, Adrián
Kefauver, Shawn C.
Fernández-Gallego, José A.
Vergara-Díaz, Omar
Nieto-Taladriz, María Teresa
Araus, José Luis
author Gracia-Romero, Adrián
author_facet Gracia-Romero, Adrián
Kefauver, Shawn C.
Fernández-Gallego, José A.
Vergara-Díaz, Omar
Nieto-Taladriz, María Teresa
Araus, José Luis
author_role author
author2 Kefauver, Shawn C.
Fernández-Gallego, José A.
Vergara-Díaz, Omar
Nieto-Taladriz, María Teresa
Araus, José Luis
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Universidad de Barcelona
Generalitat de Catalunya
Gracia-Romero, Adrián [0000-0001-8308-9693]
Kefauver, Shawn C. [0000-0002-1687-1965]
Fernandez-Gallego, J. A. [0000-0001-8928-4801]
Vergara-Díaz, Omar [0000-0001-7074-0774]
Nieto-Taladriz, María Teresa [0000-0001-6119-4249]
Araus, José Luis [0000-0002-8866-2388]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Canopy temperature
Grain yield
High-Throughput Plant Phenotyping
Multispectral
RGB
UAV
Wheat
topic Canopy temperature
Grain yield
High-Throughput Plant Phenotyping
Multispectral
RGB
UAV
Wheat
description 25 Pág.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/365962
https://api.elsevier.com/content/abstract/scopus_id/85066740556
url http://hdl.handle.net/10261/365962
https://api.elsevier.com/content/abstract/scopus_id/85066740556
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO//AGL2016-76527-R
Departamento de Protección Vegetal
https://doi.org/10.3390/rs11101244
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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