Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are...

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Autores: Rufo Gómez, Rubén, Soriano Soriano, José Miguel, Villegas, Dolors, Royo i Calpe, Conxita, Bellvert, Joaquim
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/467429
Acesso em linha:https://doi.org/10.3390/rs13061187
https://hdl.handle.net/10459.1/467429
Access Level:acceso abierto
Palavra-chave:High-throughput phenotyping
Frought stress
UAV imagery
Ground-based RGB image
Vegetation indices
Phenology
Grain yield
Biomass
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spelling Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environmentRufo Gómez, RubénSoriano Soriano, José MiguelVillegas, DolorsRoyo i Calpe, ConxitaBellvert, JoaquimHigh-throughput phenotypingFrought stressUAV imageryGround-based RGB imageVegetation indicesPhenologyGrain yieldBiomassThe adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.This study was funded by projects PID2019-109089RB-C31 and RTI2018-099949-R-C21 (Ministerio de Ciencia e Innovación, Spain). R.R. is a recipient of a PhD grant from the Spanish Ministry of Economy and Competitiveness.MDPI2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/rs13061187https://hdl.handle.net/10459.1/467429reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109089RB-C31info:eu-repo/grantAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/ RTI2018-099949-R-C21Reproducció del document publicat a: https://doi.org/10.3390/rs13061187Remote Sensing, 2021, vol. 13, num. 6cc-by, (c) Rufo et al., 2021info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/4674292026-06-24T12:42:17Z
dc.title.none.fl_str_mv Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
title Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
spellingShingle Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
Rufo Gómez, Rubén
High-throughput phenotyping
Frought stress
UAV imagery
Ground-based RGB image
Vegetation indices
Phenology
Grain yield
Biomass
title_short Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
title_full Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
title_fullStr Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
title_full_unstemmed Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
title_sort Using unmanned aerial vehicle and ground-based RGB indices to assess agronomic performance of wheat landraces and cultivars in a Mediterranean-type environment
dc.creator.none.fl_str_mv Rufo Gómez, Rubén
Soriano Soriano, José Miguel
Villegas, Dolors
Royo i Calpe, Conxita
Bellvert, Joaquim
author Rufo Gómez, Rubén
author_facet Rufo Gómez, Rubén
Soriano Soriano, José Miguel
Villegas, Dolors
Royo i Calpe, Conxita
Bellvert, Joaquim
author_role author
author2 Soriano Soriano, José Miguel
Villegas, Dolors
Royo i Calpe, Conxita
Bellvert, Joaquim
author2_role author
author
author
author
dc.subject.none.fl_str_mv High-throughput phenotyping
Frought stress
UAV imagery
Ground-based RGB image
Vegetation indices
Phenology
Grain yield
Biomass
topic High-throughput phenotyping
Frought stress
UAV imagery
Ground-based RGB image
Vegetation indices
Phenology
Grain yield
Biomass
description The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.
publishDate 2021
dc.date.none.fl_str_mv 2021
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/rs13061187
https://hdl.handle.net/10459.1/467429
url https://doi.org/10.3390/rs13061187
https://hdl.handle.net/10459.1/467429
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109089RB-C31
info:eu-repo/grantAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/ RTI2018-099949-R-C21
Reproducció del document publicat a: https://doi.org/10.3390/rs13061187
Remote Sensing, 2021, vol. 13, num. 6
dc.rights.none.fl_str_mv cc-by, (c) Rufo et al., 2021
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by, (c) Rufo et al., 2021
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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