UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
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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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 |
|
_version_ |
1808128556085018624 |