Show simple item record

dc.contributor.authorGracia-Romero, Adrian
dc.contributor.authorKefauver, Shawn C.
dc.contributor.authorFernandez-Gallego, Jose A.
dc.contributor.authorVergara-Diaz, Omar
dc.contributor.authorNieto-Taladriz, María Teresa
dc.contributor.authorAraus Ortega, José Luis
dc.date.accessioned2019-10-23T14:53:31Z
dc.date.available2019-10-23T14:53:31Z
dc.date.issued2019-05-25
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10459.1/66818
dc.description.abstractClimate 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.ca_ES
dc.description.sponsorshipThis 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.ca_ES
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relationMINECO/PN2013-2016/AGL2016-76527-Rca_ES
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/rs11101244ca_ES
dc.relation.ispartofRemote Sensing, 2019, vol. 11, num. 10, 1244ca_ES
dc.rightscc-by (c) Gracia-Romero et al., 2019ca_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectWheatca_ES
dc.subjectGrain yieldca_ES
dc.subjectHigh-Throughput Plant Phenotypingca_ES
dc.subjectCanopy temperatureca_ES
dc.titleUAV and ground image-based phenotyping: a proof of concept with durum wheatca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.3390/rs11101244


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by (c) Gracia-Romero et al., 2019
Except where otherwise noted, this item's license is described as cc-by (c) Gracia-Romero et al., 2019