English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/228013
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


Exploring UAV-imagery to support genotype selection in olive breeding programs

AuthorsRallo, Pilar; Castro, Ana Isabel de CSIC ORCID; López Granados, Francisca CSIC ORCID ; Morales Sillero, Ana; Torres-Sánchez, Jorge CSIC ORCID; Jimémez, María Rocío; Jiménez-Brenes, Francisco Manuel CSIC ORCID ; Casanova, Laura; Suárez, Mª Paz
KeywordsOlea europaea L.
Olive breeding
Plant architecture
Precision agriculture
Unmanned aerial vehicles
Issue Date17-Nov-2020
CitationScientia Horticulturae 273: 109615 (2020)
AbstractAirborne methodologies based on unmanned aerial vehicles (UAV) are becoming an extraordinary tool for implementing fast, accurate and affordable phenotyping strategies within plant breeding programs. The aim of this paper was to study the potential use of a previously developed UAV-OBIA platform, to fasten and support decision making for olive breeders regarding the selection of the most promising genotypes in terms of tree geometric traits. In particular, we have studied the feasibility of the system to efficiently classify and select olive genotypes according to four architectural parameters: tree height, crown diameter, projected crown area and canopy volume. These vegetative growth traits and their evolution during the first months after planting are key selection criteria in olive breeding programs. On-ground measurements and UAV estimations were recorded over two years (when trees were 15 and 27 months old, respectively) in two olive breeding trials using different training systems, namely intensive open vase and super high-density hedgerows. More than 1000 young trees belonging to 39 olive accessions, including new cross-bred genotypes and traditional cultivars, were assessed. Even though the accuracy in the UAV estimation compared to the on-ground measurements largely improved the second year, both methodologies detected in both years a high variability and significant differences among the studied genotypes, allowing for statistical comparisons among them. Genotype rankings based on the on-ground measures and UAV estimations were compared. The resulting Spearman’s rank coefficient correlations were very high, at above 0.85 in most cases, which highlights that very similar genotype classifications were achieved from either field-measured or airborne-estimated data. Thus, UAV imagery may be used to assess geometric traits and to develop rankings for the efficient screening and selection of genotypes in olive breeding programs.
Publisher version (URL)http://doi.org/10.1016/j.scienta.2020.109615
Identifiersdoi: 10.1016/j.scienta.2020.109615
issn: 0304-4238
Appears in Collections:(IAS) Artículos
Files in This Item:
File Description SizeFormat 
olive_breeding.pdf Embargoed until November 17, 20221,07 MBAdobe PDFThumbnail
View/Open    Request a copy
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.