Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Wouters, Jonathas Mateus
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
Brasil
Agronomia
UFSM
Programa de Pós-Graduação em Agricultura de Precisão
Centro de Ciências Rurais
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Drone
Alta resolução espacial
Processamento digital de imagens
Análise estatística
Agricultura de precisão
High spatial resolution
Digital image processing
Statistical analysis
Precision agriculture
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
Link de acesso: http://repositorio.ufsm.br/handle/1/23562
Resumo: Currently, there is a great use of remotely controlled aircraft in agriculture and the need to increasingly optimize the production of Brazilian crops. To this end, the work seeks to analyze the failures in sowing / planting in soybean crops from the interpretation of images obtained with this type of equipment. In this work, the images were obtained at flight heights of 60, 90 and 120 meters, and on four post-planting dates, 15, 22, 32 and 37 days after sowing, the processing was performed in the QGIS software generating images with the percentage of coverage by soybean plants. Analyzing the classified images it was possible to estimate the development of soybean plants, it was found that there was no significant difference between the flight heights, so the best time to evaluate sowing failures was 120 meters, as it allows a larger area covered on the same flight. The flight that best represented the coverage percentage of soybean plants was the fourth (37 after sowing), since it occurred right after the effect of a herbicide application making the classification more efficient without the presence of weeds.
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spelling Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remotoEstimation of seeding failures in soybean culture with images from non-crewed air vehiclesDroneAlta resolução espacialProcessamento digital de imagensAnálise estatísticaAgricultura de precisãoHigh spatial resolutionDigital image processingStatistical analysisPrecision agricultureCNPQ::CIENCIAS AGRARIAS::AGRONOMIACurrently, there is a great use of remotely controlled aircraft in agriculture and the need to increasingly optimize the production of Brazilian crops. To this end, the work seeks to analyze the failures in sowing / planting in soybean crops from the interpretation of images obtained with this type of equipment. In this work, the images were obtained at flight heights of 60, 90 and 120 meters, and on four post-planting dates, 15, 22, 32 and 37 days after sowing, the processing was performed in the QGIS software generating images with the percentage of coverage by soybean plants. Analyzing the classified images it was possible to estimate the development of soybean plants, it was found that there was no significant difference between the flight heights, so the best time to evaluate sowing failures was 120 meters, as it allows a larger area covered on the same flight. The flight that best represented the coverage percentage of soybean plants was the fourth (37 after sowing), since it occurred right after the effect of a herbicide application making the classification more efficient without the presence of weeds.Atualmente, destaca-se o grande uso de aeronaves controladas remotamente na agricultura e a necessidade de cada vez mais otimizar a produção das lavouras brasileiras. Com esse intuito, o trabalho busca analisar as falhas na semeadura/plantio em lavoura de soja a partir da interpretação de imagens obtidas com este tipo de equipamento. Neste trabalho, as imagens foram obtidas nas alturas de voo de 60, 90 e 120 metros, e em quatro datas pós-plantio, 15, 22, 32 e 37 dias após a semeadura, o processamento foi realizado no software QGIS gerando imagens com o percentual de cobertura pelas plantas de soja. Analisando as imagens classificadas foi possível estimar o desenvolvimento das plantas de soja, constatou-se que não houve diferença significativa entre as alturas de voo, sendo assim a melhor altura para avaliar as falhas de semeadura foi a de 120 metros, por possibilitar uma maior área coberta em um mesmo voo. O voo que melhor representou o percentual de cobertura das plantas de soja foi o quarto (37 após a semeadura), visto que ocorreu logo após o efeito de uma aplicação de herbicida tornando a classificação mais eficiente sem a presença de daninhas.Universidade Federal de Santa MariaBrasilAgronomiaUFSMPrograma de Pós-Graduação em Agricultura de PrecisãoCentro de Ciências RuraisSebem, Elódiohttp://lattes.cnpq.br/7879588106056349Miola, Alessandro CarvalhoVian, André LuísWouters, Jonathas Mateus2022-01-18T13:02:28Z2022-01-18T13:02:28Z2020-08-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/23562porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2022-02-04T18:43:06Zoai:repositorio.ufsm.br:1/23562Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/PUBhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.com||manancial@ufsm.bropendoar:2022-02-04T18:43:06Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false
dc.title.none.fl_str_mv Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
Estimation of seeding failures in soybean culture with images from non-crewed air vehicles
title Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
spellingShingle Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
Wouters, Jonathas Mateus
Drone
Alta resolução espacial
Processamento digital de imagens
Análise estatística
Agricultura de precisão
High spatial resolution
Digital image processing
Statistical analysis
Precision agriculture
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
title_short Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
title_full Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
title_fullStr Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
title_full_unstemmed Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
title_sort Estimativa de falhas em semeadura de soja (Glycine Max (L) Merrill) a partir de imagens de sensoriamento remoto
author Wouters, Jonathas Mateus
author_facet Wouters, Jonathas Mateus
author_role author
dc.contributor.none.fl_str_mv Sebem, Elódio
http://lattes.cnpq.br/7879588106056349
Miola, Alessandro Carvalho
Vian, André Luís
dc.contributor.author.fl_str_mv Wouters, Jonathas Mateus
dc.subject.por.fl_str_mv Drone
Alta resolução espacial
Processamento digital de imagens
Análise estatística
Agricultura de precisão
High spatial resolution
Digital image processing
Statistical analysis
Precision agriculture
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
topic Drone
Alta resolução espacial
Processamento digital de imagens
Análise estatística
Agricultura de precisão
High spatial resolution
Digital image processing
Statistical analysis
Precision agriculture
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
description Currently, there is a great use of remotely controlled aircraft in agriculture and the need to increasingly optimize the production of Brazilian crops. To this end, the work seeks to analyze the failures in sowing / planting in soybean crops from the interpretation of images obtained with this type of equipment. In this work, the images were obtained at flight heights of 60, 90 and 120 meters, and on four post-planting dates, 15, 22, 32 and 37 days after sowing, the processing was performed in the QGIS software generating images with the percentage of coverage by soybean plants. Analyzing the classified images it was possible to estimate the development of soybean plants, it was found that there was no significant difference between the flight heights, so the best time to evaluate sowing failures was 120 meters, as it allows a larger area covered on the same flight. The flight that best represented the coverage percentage of soybean plants was the fourth (37 after sowing), since it occurred right after the effect of a herbicide application making the classification more efficient without the presence of weeds.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-26
2022-01-18T13:02:28Z
2022-01-18T13:02:28Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufsm.br/handle/1/23562
url http://repositorio.ufsm.br/handle/1/23562
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
Brasil
Agronomia
UFSM
Programa de Pós-Graduação em Agricultura de Precisão
Centro de Ciências Rurais
publisher.none.fl_str_mv Universidade Federal de Santa Maria
Brasil
Agronomia
UFSM
Programa de Pós-Graduação em Agricultura de Precisão
Centro de Ciências Rurais
dc.source.none.fl_str_mv reponame:Manancial - Repositório Digital da UFSM
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Manancial - Repositório Digital da UFSM
collection Manancial - Repositório Digital da UFSM
repository.name.fl_str_mv Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)
repository.mail.fl_str_mv atendimento.sib@ufsm.br||tedebc@gmail.com||manancial@ufsm.br
_version_ 1830834922102718464

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