Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna
Ano de defesa: | 2022 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Ciências Rurais |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Florestal
|
Departamento: |
Recursos Florestais e Engenharia Florestal
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/26770 |
Resumo: | The management of Eucalyptus production has its technical operational processes well defined and consolidated throughout the country. However, the management of weeds, which compete with Eucalyptus plants, decrease the final productivity of the plantation, this monitoring of weed control is still dependent on a technical inspection in loco and its quantification is not accurate. Therefore, the present study aims to map the density of weeds in commercial plantations of Eucalyptus saligna through artificial intelligence techniques applied to multispectral images of very high spatial resolution. Thus, a study was developed based on a bibliometric review on the state of the art of the research developed with RPAS (Remotely Piloted Aircraft System) for the mapping weeds in forest and agricultural areas. In four Eucalyptus saligna production areas in the state of Rio Grande do Sul, Brazil, with an average age of 54 days after planting, eight sample plots were evaluated to identify and obtain hyperspectral reflectances readings of weeds and Eucalyptus saligna with the FieldSpec® 3 spectroradiometer. Using the artificial intelligence RF (Random Forest) algorithm with an accuracy of 95.44%, it was determined that the most important wavelength ranges are from 510 to 589 nm, 400 to 423 nm, 674 to 731 nm and 886 at 900 nm were able to distinguish weeds from Eucalyptus saligna individuals in commercial plantations. In these same areas, multispectral images were also obtained with the Parrot Sequoia sensor embedded in the RPAS Phantom 4 Pro, using a flight height of 30 m. From these images, the four sensor bands and five more vegetation indices were used as predictors. The K-Means algorithm was applied for image segmentation and vegetation discrimination in the classes Eucalyptus saligna, weeds and regrowth of Eucalyptus saligna. These data were partitioned into 70% training and 30% testing, to be modeled by the RF algorithm, whose model obtained an accuracy of 95.49% in the classification of weeds, which enabled the elaboration of the weed density map for the study areas, composing the final product of the study |
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2022-11-04T21:49:13Z2022-11-04T21:49:13Z2022-08-31http://repositorio.ufsm.br/handle/1/26770The management of Eucalyptus production has its technical operational processes well defined and consolidated throughout the country. However, the management of weeds, which compete with Eucalyptus plants, decrease the final productivity of the plantation, this monitoring of weed control is still dependent on a technical inspection in loco and its quantification is not accurate. Therefore, the present study aims to map the density of weeds in commercial plantations of Eucalyptus saligna through artificial intelligence techniques applied to multispectral images of very high spatial resolution. Thus, a study was developed based on a bibliometric review on the state of the art of the research developed with RPAS (Remotely Piloted Aircraft System) for the mapping weeds in forest and agricultural areas. In four Eucalyptus saligna production areas in the state of Rio Grande do Sul, Brazil, with an average age of 54 days after planting, eight sample plots were evaluated to identify and obtain hyperspectral reflectances readings of weeds and Eucalyptus saligna with the FieldSpec® 3 spectroradiometer. Using the artificial intelligence RF (Random Forest) algorithm with an accuracy of 95.44%, it was determined that the most important wavelength ranges are from 510 to 589 nm, 400 to 423 nm, 674 to 731 nm and 886 at 900 nm were able to distinguish weeds from Eucalyptus saligna individuals in commercial plantations. In these same areas, multispectral images were also obtained with the Parrot Sequoia sensor embedded in the RPAS Phantom 4 Pro, using a flight height of 30 m. From these images, the four sensor bands and five more vegetation indices were used as predictors. The K-Means algorithm was applied for image segmentation and vegetation discrimination in the classes Eucalyptus saligna, weeds and regrowth of Eucalyptus saligna. These data were partitioned into 70% training and 30% testing, to be modeled by the RF algorithm, whose model obtained an accuracy of 95.49% in the classification of weeds, which enabled the elaboration of the weed density map for the study areas, composing the final product of the studyO manejo da produção do Eucalyptus possui seus processos técnicos operacionais bem definidos e consolidados em todo o país. No entanto, o manejo das plantas espontâneas, que competem com as plantas de Eucalyptus, diminuem a produtividade final do plantio, esse monitoramento de controle de plantas espontâneas ainda é dependente de uma vistoria técnica in loco e sua quantificação não é precisa. Sendo assim, o presente estudo tem como objetivo mapear a densidade de plantas espontâneas em plantios comerciais de Eucalyptus saligna por meio de técnicas de inteligência artificial aplicadas a imagens multiespectrais de altíssima resolução espacial. Com isso foi desenvolvido um estudo a partir de uma revisão bibliométrica sobre o estado da arte as pesquisas desenvolvidas com RPAS (Remotely Piloted Aircraft System) para o mapeamento das plantas espontâneas em áreas florestais e agrícolas. Em quatro áreas de produção do Eucalyptus saligna no estado do Rio Grande do Sul, Brasil, com idade média de 54 dias após o plantio, foram avaliadas oito parcelas amostrais para identificação e obtenção de leituras de refletâncias hiperespectrais de plantas espontâneas e Eucalyptus saligna com o espectrorradiômetro FieldSpec® 3. Por meio do algoritmo de inteligência artificial RF (Random Forest), com precisão de 95,44% determinou-se que os intervalos de comprimento de onda de maior importância são de 510 a 589 nm, 400 a 423 nm, 674 a 731 nm e 886 a 900 nm foram capazes de distinguir plantas espontâneas de indivíduos de Eucalyptus saligna em plantios comerciais. Nessas mesmas áreas também foram obtidas imagens multiespectrais com o sensor Parrot Sequoia embarcado no RPAS Phantom 4 Pro, utilizando a altura do voo de 30 m. A partir dessas imagens, foram utilizados como preditores as quatro bandas do sensor e mais cinco índices de vegetação. O algoritmo K-Means foi aplicado para segmentação das imagens e discriminação da vegetação nas classes Eucalyptus saligna, plantas espontâneas e rebrota de Eucalyptus saligna. Esses dados foram particionados em 70% treino e 30% teste, para serem modelados pelo algoritmo RF, cujo modelo obteve uma precisão de 95,49% de acerto na classificação de plantas espontâneas, o que viabilizou a elaboração do mapa de densidade de plantas espontâneas para as áreas do estudo, compondo o produto final do estudo.porUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Engenharia FlorestalUFSMBrasilRecursos Florestais e Engenharia FlorestalAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessRPASInteligência artificialPlantas espontâneasEucalyptusArtificial intelligenceWeedsCNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTALImagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus salignaMultispectral images and artificial intelligence for predicting weed density in planting of Eucalyptus salignainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisPereira, Rudiney Soareshttp://lattes.cnpq.br/9479801378014588Longhi, Solon JonasMiola, Alessandro CarvalhoEugenio, Fernando CoelhoPes, Luciano ZucuniTeixeira, Tiago De Gregorihttp://lattes.cnpq.br/7867596036622027Fernandes, Pablo500200000003600600600600600600600600a7274a7d-8dcd-466b-9a0d-c2b629e2ca88fc01908f-095b-4418-b267-7e170cc003fe4dc7a123-f11d-41db-8bfb-7861467d2f9b6a4811e3-4f1d-4730-b817-935e94cd9f20bddf88f0-de82-43f5-a2d9-a145d0816a4ba632796c-f184-41d7-9163-1e78fc6ff2db78b0ede1-f4b7-49d2-a9be-832995d63dc0reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGEF_2022_FERNANDES_PABLO.pdfTES_PPGEF_2022_FERNANDES_PABLO.pdfTese de doutoradoapplication/pdf7952514http://repositorio.ufsm.br/bitstream/1/26770/1/TES_PPGEF_2022_FERNANDES_PABLO.pdf313ca19290e1c13a4fae2be32a97770fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
dc.title.alternative.eng.fl_str_mv |
Multispectral images and artificial intelligence for predicting weed density in planting of Eucalyptus saligna |
title |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
spellingShingle |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna Fernandes, Pablo RPAS Inteligência artificial Plantas espontâneas Eucalyptus Artificial intelligence Weeds CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
title_short |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
title_full |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
title_fullStr |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
title_full_unstemmed |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
title_sort |
Imagens multiespectrais e inteligência artificial para predição da densidade de plantas espontâneas em plantio de Eucalyptus saligna |
author |
Fernandes, Pablo |
author_facet |
Fernandes, Pablo |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Pereira, Rudiney Soares |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9479801378014588 |
dc.contributor.advisor-co1.fl_str_mv |
Longhi, Solon Jonas |
dc.contributor.referee1.fl_str_mv |
Miola, Alessandro Carvalho |
dc.contributor.referee2.fl_str_mv |
Eugenio, Fernando Coelho |
dc.contributor.referee3.fl_str_mv |
Pes, Luciano Zucuni |
dc.contributor.referee4.fl_str_mv |
Teixeira, Tiago De Gregori |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7867596036622027 |
dc.contributor.author.fl_str_mv |
Fernandes, Pablo |
contributor_str_mv |
Pereira, Rudiney Soares Longhi, Solon Jonas Miola, Alessandro Carvalho Eugenio, Fernando Coelho Pes, Luciano Zucuni Teixeira, Tiago De Gregori |
dc.subject.por.fl_str_mv |
RPAS Inteligência artificial Plantas espontâneas Eucalyptus |
topic |
RPAS Inteligência artificial Plantas espontâneas Eucalyptus Artificial intelligence Weeds CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
dc.subject.eng.fl_str_mv |
Artificial intelligence Weeds |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
description |
The management of Eucalyptus production has its technical operational processes well defined and consolidated throughout the country. However, the management of weeds, which compete with Eucalyptus plants, decrease the final productivity of the plantation, this monitoring of weed control is still dependent on a technical inspection in loco and its quantification is not accurate. Therefore, the present study aims to map the density of weeds in commercial plantations of Eucalyptus saligna through artificial intelligence techniques applied to multispectral images of very high spatial resolution. Thus, a study was developed based on a bibliometric review on the state of the art of the research developed with RPAS (Remotely Piloted Aircraft System) for the mapping weeds in forest and agricultural areas. In four Eucalyptus saligna production areas in the state of Rio Grande do Sul, Brazil, with an average age of 54 days after planting, eight sample plots were evaluated to identify and obtain hyperspectral reflectances readings of weeds and Eucalyptus saligna with the FieldSpec® 3 spectroradiometer. Using the artificial intelligence RF (Random Forest) algorithm with an accuracy of 95.44%, it was determined that the most important wavelength ranges are from 510 to 589 nm, 400 to 423 nm, 674 to 731 nm and 886 at 900 nm were able to distinguish weeds from Eucalyptus saligna individuals in commercial plantations. In these same areas, multispectral images were also obtained with the Parrot Sequoia sensor embedded in the RPAS Phantom 4 Pro, using a flight height of 30 m. From these images, the four sensor bands and five more vegetation indices were used as predictors. The K-Means algorithm was applied for image segmentation and vegetation discrimination in the classes Eucalyptus saligna, weeds and regrowth of Eucalyptus saligna. These data were partitioned into 70% training and 30% testing, to be modeled by the RF algorithm, whose model obtained an accuracy of 95.49% in the classification of weeds, which enabled the elaboration of the weed density map for the study areas, composing the final product of the study |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-11-04T21:49:13Z |
dc.date.available.fl_str_mv |
2022-11-04T21:49:13Z |
dc.date.issued.fl_str_mv |
2022-08-31 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/26770 |
url |
http://repositorio.ufsm.br/handle/1/26770 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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500200000003 |
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600 600 600 600 600 600 600 600 |
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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.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Florestal |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Recursos Florestais e Engenharia Florestal |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
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