UAV and ground image-based phenotyping: a proof of concept with durum wheat
| dc.contributor.author | Gracia-Romero, Adrian | |
| dc.contributor.author | Kefauver, Shawn C. | |
| dc.contributor.author | Fernandez-Gallego, Jose A. | |
| dc.contributor.author | Vergara-Diaz, Omar | |
| dc.contributor.author | Nieto-Taladriz, María Teresa | |
| dc.contributor.author | Araus Ortega, José Luis | |
| dc.date.accessioned | 2019-10-23T14:53:31Z | |
| dc.date.available | 2019-10-23T14:53:31Z | |
| dc.date.issued | 2019-05-25 | |
| dc.description.abstract | 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. | ca_ES |
| dc.description.sponsorship | 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. | ca_ES |
| dc.identifier.doi | https://doi.org/10.3390/rs11101244 | |
| dc.identifier.issn | 2072-4292 | |
| dc.identifier.uri | http://hdl.handle.net/10459.1/66818 | |
| dc.language.iso | eng | ca_ES |
| dc.publisher | MDPI | ca_ES |
| dc.relation | info:eu-repo/grantAgreement/MINECO//AGL2016-76527-R/ES/ | ca_ES |
| dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.3390/rs11101244 | ca_ES |
| dc.relation.ispartof | Remote Sensing, 2019, vol. 11, num. 10, 1244 | ca_ES |
| dc.rights | cc-by (c) Gracia-Romero et al., 2019 | ca_ES |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_ES |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Wheat | ca_ES |
| dc.subject | Grain yield | ca_ES |
| dc.subject | High-Throughput Plant Phenotyping | ca_ES |
| dc.subject | Canopy temperature | ca_ES |
| dc.title | UAV and ground image-based phenotyping: a proof of concept with durum wheat | ca_ES |
| dc.type | info:eu-repo/semantics/article | ca_ES |
| dc.type.version | info:eu-repo/semantics/publishedVersion | ca_ES |