Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote s...

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Autores: Buchaillot, Ma. Luisa, Gracia-Romero, Adrian, Vergara-Diaz, Omar, Zaman-Allah, Mainassara A., Tarekegne, Amsal, Cairns, Jill E., Prasanna, Boddupalli M., Araus Ortega, José Luis, Kefauver, Shawn C.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/66781
Acceso en línea:https://doi.org/10.3390/s19081815
http://hdl.handle.net/10459.1/66781
Access Level:acceso abierto
Palabra clave:Phenotyping
Remote sensing
Nitrogen
Àfrica
Blat de moro
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spelling Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniquesBuchaillot, Ma. LuisaGracia-Romero, AdrianVergara-Diaz, OmarZaman-Allah, Mainassara A.Tarekegne, AmsalCairns, Jill E.Prasanna, Boddupalli M.Araus Ortega, José LuisKefauver, Shawn C.PhenotypingRemote sensingNitrogenÀfricaBlat de moroMaize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.This research and APC was funded by Bill & Melinda Gates Foundation and USAID Stress Tolerant Maize for Africa program, grant number [OPP1134248], and the MAIZE CGIAR research program. The CGIAR Research Program MAIZE receives W1&W2 support from the Governments of Australia, Belgium, Canada, China, France, India, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Sweden, Switzerland, U.K., U.S., and the World Bank.MDPI2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/s19081815http://hdl.handle.net/10459.1/66781reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a: https://doi.org/10.3390/s19081815Sensors, 2019, vol. 19, núm. 8, 1815cc-by (c) Buchaillot et al., 2019info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/667812026-06-24T12:42:17Z
dc.title.none.fl_str_mv Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
title Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
spellingShingle Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
Buchaillot, Ma. Luisa
Phenotyping
Remote sensing
Nitrogen
Àfrica
Blat de moro
title_short Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
title_full Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
title_fullStr Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
title_full_unstemmed Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
title_sort Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques
dc.creator.none.fl_str_mv Buchaillot, Ma. Luisa
Gracia-Romero, Adrian
Vergara-Diaz, Omar
Zaman-Allah, Mainassara A.
Tarekegne, Amsal
Cairns, Jill E.
Prasanna, Boddupalli M.
Araus Ortega, José Luis
Kefauver, Shawn C.
author Buchaillot, Ma. Luisa
author_facet Buchaillot, Ma. Luisa
Gracia-Romero, Adrian
Vergara-Diaz, Omar
Zaman-Allah, Mainassara A.
Tarekegne, Amsal
Cairns, Jill E.
Prasanna, Boddupalli M.
Araus Ortega, José Luis
Kefauver, Shawn C.
author_role author
author2 Gracia-Romero, Adrian
Vergara-Diaz, Omar
Zaman-Allah, Mainassara A.
Tarekegne, Amsal
Cairns, Jill E.
Prasanna, Boddupalli M.
Araus Ortega, José Luis
Kefauver, Shawn C.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Phenotyping
Remote sensing
Nitrogen
Àfrica
Blat de moro
topic Phenotyping
Remote sensing
Nitrogen
Àfrica
Blat de moro
description Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.
publishDate 2019
dc.date.none.fl_str_mv 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/s19081815
http://hdl.handle.net/10459.1/66781
url https://doi.org/10.3390/s19081815
http://hdl.handle.net/10459.1/66781
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/s19081815
Sensors, 2019, vol. 19, núm. 8, 1815
dc.rights.none.fl_str_mv cc-by (c) Buchaillot et al., 2019
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Buchaillot et al., 2019
http://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: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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