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

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) hav...

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Autores: Gracia-Romero, Adrian, Kefauver, Shawn C., Fernandez-Gallego, Jose A., Vergara-Diaz, Omar, Nieto-Taladriz, María Teresa, Araus Ortega, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/66818
Acceso en línea:https://doi.org/10.3390/rs11101244
http://hdl.handle.net/10459.1/66818
Access Level:acceso abierto
Palabra clave:Wheat
Grain yield
High-Throughput Plant Phenotyping
Canopy temperature
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spelling UAV and ground image-based phenotyping: a proof of concept with durum wheatGracia-Romero, AdrianKefauver, Shawn C.Fernandez-Gallego, Jose A.Vergara-Diaz, OmarNieto-Taladriz, María TeresaAraus Ortega, José LuisWheatGrain yieldHigh-Throughput Plant PhenotypingCanopy temperatureClimate 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.MDPI201920192019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/rs11101244http://hdl.handle.net/10459.1/66818http://hdl.handle.net/10459.1/66818reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/grantAgreement/MINECO//AGL2016-76527-RReproducció del document publicat a: https://doi.org/10.3390/rs11101244Remote Sensing, 2019, vol. 11, num. 10, 1244cc-by (c) Gracia-Romero et al., 2019info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/668182026-05-29T05:05:01Z
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, Adrian
Wheat
Grain yield
High-Throughput Plant Phenotyping
Canopy temperature
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, Adrian
Kefauver, Shawn C.
Fernandez-Gallego, Jose A.
Vergara-Diaz, Omar
Nieto-Taladriz, María Teresa
Araus Ortega, José Luis
author Gracia-Romero, Adrian
author_facet Gracia-Romero, Adrian
Kefauver, Shawn C.
Fernandez-Gallego, Jose A.
Vergara-Diaz, Omar
Nieto-Taladriz, María Teresa
Araus Ortega, José Luis
author_role author
author2 Kefauver, Shawn C.
Fernandez-Gallego, Jose A.
Vergara-Diaz, Omar
Nieto-Taladriz, María Teresa
Araus Ortega, José Luis
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Wheat
Grain yield
High-Throughput Plant Phenotyping
Canopy temperature
topic Wheat
Grain yield
High-Throughput Plant Phenotyping
Canopy temperature
description 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/rs11101244
http://hdl.handle.net/10459.1/66818
http://hdl.handle.net/10459.1/66818
url https://doi.org/10.3390/rs11101244
http://hdl.handle.net/10459.1/66818
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO//AGL2016-76527-R
Reproducció del document publicat a: https://doi.org/10.3390/rs11101244
Remote Sensing, 2019, vol. 11, num. 10, 1244
dc.rights.none.fl_str_mv cc-by (c) Gracia-Romero et al., 2019
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Gracia-Romero et al., 2019
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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