UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing

Detalhes bibliográficos
Autor(a) principal: Santana, Dthenifer Cordeiro
Data de Publicação: 2021
Outros Autores: Cotrim, Mayara Favero [UNESP], Flores, Marcela Silva, Rojo Baio, Fabio Henrique, Shiratsuchi, Luciano Shozo, Silva Junior, Carlos Antonio da, Teodoro, Larissa Pereira Ribeiro, Teodoro, Paulo Eduardo [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.rsase.2021.100534
http://hdl.handle.net/11449/206361
Resumo: Carrying out monitoring during the crop cycle through vegetation indices (VIs) with obtained unmanned aerial vehicle allows agility in decisions about management practices, as well as concerning nutritional deficiencies in crops, as nitrogen (N). This nutrient absorbed in greater quantity, and that most influences the grain yield in corn. This research hypothesized that different N topdressing levels can affect the agronomic performance of corn varieties and that those effects can be expressed by VIs. The objective was to evaluate the use of VIs in the monitoring of corn varieties submitted to different N levels. Two experiments were carried out in a randomized block design with three replicates in a factorial scheme, replicated for two crop seasons (2017/2018 and 2018/2019). The first factor was composed of 11 cultivars of corn. The second factor was composed of two contrasting N levels (60 kg ha−1 - low and 180 kg ha−1 - high). Vegetation indices (NDVI and NDRE) and agronomic traits (leaf N content, plant height, ear insertion height, stem diameter, ear length, number of rows per ear, number of grains per row, and grain yield) were evaluated. Our findings allow us to understand how top dressing can influence the agronomic performance of corn genotypes and their relationship with UAV-vegetation indices in two crop seasons using Sensefly Sequoia multispectral sensor. High N topdressing levels provides better agronomic and spectral response in corn, regardless of the variety used. This behavior can be confirmed through the NDVI and NDRE. High N topdressing levels provides a positive correlation between the VIs evaluated (NDVI and NDRE) with the grain yield in corn.
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spelling UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressingFertilization levelsGrain yieldRemote sensing applicationZea mays LCarrying out monitoring during the crop cycle through vegetation indices (VIs) with obtained unmanned aerial vehicle allows agility in decisions about management practices, as well as concerning nutritional deficiencies in crops, as nitrogen (N). This nutrient absorbed in greater quantity, and that most influences the grain yield in corn. This research hypothesized that different N topdressing levels can affect the agronomic performance of corn varieties and that those effects can be expressed by VIs. The objective was to evaluate the use of VIs in the monitoring of corn varieties submitted to different N levels. Two experiments were carried out in a randomized block design with three replicates in a factorial scheme, replicated for two crop seasons (2017/2018 and 2018/2019). The first factor was composed of 11 cultivars of corn. The second factor was composed of two contrasting N levels (60 kg ha−1 - low and 180 kg ha−1 - high). Vegetation indices (NDVI and NDRE) and agronomic traits (leaf N content, plant height, ear insertion height, stem diameter, ear length, number of rows per ear, number of grains per row, and grain yield) were evaluated. Our findings allow us to understand how top dressing can influence the agronomic performance of corn genotypes and their relationship with UAV-vegetation indices in two crop seasons using Sensefly Sequoia multispectral sensor. High N topdressing levels provides better agronomic and spectral response in corn, regardless of the variety used. This behavior can be confirmed through the NDVI and NDRE. High N topdressing levels provides a positive correlation between the VIs evaluated (NDVI and NDRE) with the grain yield in corn.State University of Mato Grosso Do Sul (UEMS)State University of São Paulo (UNESP)Federal University of Mato Grosso Do Sul (UFMS)Louisiana State University (LSU) AgCenter School of Plant Environmental and Soil SciencesState University of Mato Grosso (UNEMAT) Departmente of GeographyState University of São Paulo (UNESP)Universidade Estadual de Mato Grosso do Sul (UEMS)Universidade Estadual Paulista (Unesp)Universidade Federal de Mato Grosso do Sul (UFMS)and Soil SciencesState University of Mato Grosso (UNEMAT)Santana, Dthenifer CordeiroCotrim, Mayara Favero [UNESP]Flores, Marcela SilvaRojo Baio, Fabio HenriqueShiratsuchi, Luciano ShozoSilva Junior, Carlos Antonio daTeodoro, Larissa Pereira RibeiroTeodoro, Paulo Eduardo [UNESP]2021-06-25T10:30:46Z2021-06-25T10:30:46Z2021-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.rsase.2021.100534Remote Sensing Applications: Society and Environment, v. 23.2352-9385http://hdl.handle.net/11449/20636110.1016/j.rsase.2021.1005342-s2.0-85106215315Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing Applications: Society and Environmentinfo:eu-repo/semantics/openAccess2021-10-23T04:16:22Zoai:repositorio.unesp.br:11449/206361Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:44:43.074901Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
title UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
spellingShingle UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
Santana, Dthenifer Cordeiro
Fertilization levels
Grain yield
Remote sensing application
Zea mays L
title_short UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
title_full UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
title_fullStr UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
title_full_unstemmed UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
title_sort UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
author Santana, Dthenifer Cordeiro
author_facet Santana, Dthenifer Cordeiro
Cotrim, Mayara Favero [UNESP]
Flores, Marcela Silva
Rojo Baio, Fabio Henrique
Shiratsuchi, Luciano Shozo
Silva Junior, Carlos Antonio da
Teodoro, Larissa Pereira Ribeiro
Teodoro, Paulo Eduardo [UNESP]
author_role author
author2 Cotrim, Mayara Favero [UNESP]
Flores, Marcela Silva
Rojo Baio, Fabio Henrique
Shiratsuchi, Luciano Shozo
Silva Junior, Carlos Antonio da
Teodoro, Larissa Pereira Ribeiro
Teodoro, Paulo Eduardo [UNESP]
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Mato Grosso do Sul (UEMS)
Universidade Estadual Paulista (Unesp)
Universidade Federal de Mato Grosso do Sul (UFMS)
and Soil Sciences
State University of Mato Grosso (UNEMAT)
dc.contributor.author.fl_str_mv Santana, Dthenifer Cordeiro
Cotrim, Mayara Favero [UNESP]
Flores, Marcela Silva
Rojo Baio, Fabio Henrique
Shiratsuchi, Luciano Shozo
Silva Junior, Carlos Antonio da
Teodoro, Larissa Pereira Ribeiro
Teodoro, Paulo Eduardo [UNESP]
dc.subject.por.fl_str_mv Fertilization levels
Grain yield
Remote sensing application
Zea mays L
topic Fertilization levels
Grain yield
Remote sensing application
Zea mays L
description Carrying out monitoring during the crop cycle through vegetation indices (VIs) with obtained unmanned aerial vehicle allows agility in decisions about management practices, as well as concerning nutritional deficiencies in crops, as nitrogen (N). This nutrient absorbed in greater quantity, and that most influences the grain yield in corn. This research hypothesized that different N topdressing levels can affect the agronomic performance of corn varieties and that those effects can be expressed by VIs. The objective was to evaluate the use of VIs in the monitoring of corn varieties submitted to different N levels. Two experiments were carried out in a randomized block design with three replicates in a factorial scheme, replicated for two crop seasons (2017/2018 and 2018/2019). The first factor was composed of 11 cultivars of corn. The second factor was composed of two contrasting N levels (60 kg ha−1 - low and 180 kg ha−1 - high). Vegetation indices (NDVI and NDRE) and agronomic traits (leaf N content, plant height, ear insertion height, stem diameter, ear length, number of rows per ear, number of grains per row, and grain yield) were evaluated. Our findings allow us to understand how top dressing can influence the agronomic performance of corn genotypes and their relationship with UAV-vegetation indices in two crop seasons using Sensefly Sequoia multispectral sensor. High N topdressing levels provides better agronomic and spectral response in corn, regardless of the variety used. This behavior can be confirmed through the NDVI and NDRE. High N topdressing levels provides a positive correlation between the VIs evaluated (NDVI and NDRE) with the grain yield in corn.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T10:30:46Z
2021-06-25T10:30:46Z
2021-08-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.rsase.2021.100534
Remote Sensing Applications: Society and Environment, v. 23.
2352-9385
http://hdl.handle.net/11449/206361
10.1016/j.rsase.2021.100534
2-s2.0-85106215315
url http://dx.doi.org/10.1016/j.rsase.2021.100534
http://hdl.handle.net/11449/206361
identifier_str_mv Remote Sensing Applications: Society and Environment, v. 23.
2352-9385
10.1016/j.rsase.2021.100534
2-s2.0-85106215315
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Remote Sensing Applications: Society and Environment
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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