Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/30420 |
Resumo: | Agriculture is under intense revolution, numerous data is generated every moment, either by the farmers, sensors, or by generating new products for the agricultural sector, favoring digital agriculture. Thus, the objectives of this work were to collect field data, as well as use available data such as remote sensing and geospatial public data to generate knowledge for agriculture of Rio Grande do Sul (RS), Brazil. The objectives of the first study were to i) evaluate of the spatial variability of field data to generate the crop classification model; ii) evaluate the transfer learning model with the subsequent growing season data; iii) evaluate the accuracy of the forecast model for early forecasts, and IV) develop a classification and mapping model of agricultural crops for RS. The objectives of the second study were to: i) compare data generated through simulations of development of agricultural crops and field data; II) evaluate production fields masks generated by the Rural Environmental Registry, MapBiomas and random forest model; and iii) evaluate non-supervised classification models, supervised classification with data from agricultural crop development simulations, and supervised classification with field data, as well as their combination. The objectives of the third study were to: i) map monoculture patterns and crop rotation in the different mesoregions of the state of RS; ii) identify soil and climate variables that coincides with the highest percentages of monoculture area; iii) evaluate the effect of crop rotation on crop grain yields. As a result of the first study, the model of classification and mapping of agricultural crops of RS were generated, with the possibility of transfer learning to subsequent growing seasons, obtaining predictions from January 1 of the agricultural crop, increasing accuracy as more remote sensing images of the crops are captured. Also, in the second study it was possible to generate models of classification of crop types with different models, nonsupervisioned classification, supervised classification with field data, simulations of crop development models, and adding field data and simulations data to increase the accuracy of the model. Crop rotation mapping and crop rotation patterns for the state of RS were generated by enabling a more holistic look at the adoption of crop rotation strategies, intensification, and sustainability of agriculture to the state. The results presented in this study have the potential to contribute to digitization in agriculture, and may assist farmers, and policymakers during the decision-making process. |
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2023-11-01T11:50:13Z2023-11-01T11:50:13Z2023-08-23http://repositorio.ufsm.br/handle/1/30420Agriculture is under intense revolution, numerous data is generated every moment, either by the farmers, sensors, or by generating new products for the agricultural sector, favoring digital agriculture. Thus, the objectives of this work were to collect field data, as well as use available data such as remote sensing and geospatial public data to generate knowledge for agriculture of Rio Grande do Sul (RS), Brazil. The objectives of the first study were to i) evaluate of the spatial variability of field data to generate the crop classification model; ii) evaluate the transfer learning model with the subsequent growing season data; iii) evaluate the accuracy of the forecast model for early forecasts, and IV) develop a classification and mapping model of agricultural crops for RS. The objectives of the second study were to: i) compare data generated through simulations of development of agricultural crops and field data; II) evaluate production fields masks generated by the Rural Environmental Registry, MapBiomas and random forest model; and iii) evaluate non-supervised classification models, supervised classification with data from agricultural crop development simulations, and supervised classification with field data, as well as their combination. The objectives of the third study were to: i) map monoculture patterns and crop rotation in the different mesoregions of the state of RS; ii) identify soil and climate variables that coincides with the highest percentages of monoculture area; iii) evaluate the effect of crop rotation on crop grain yields. As a result of the first study, the model of classification and mapping of agricultural crops of RS were generated, with the possibility of transfer learning to subsequent growing seasons, obtaining predictions from January 1 of the agricultural crop, increasing accuracy as more remote sensing images of the crops are captured. Also, in the second study it was possible to generate models of classification of crop types with different models, nonsupervisioned classification, supervised classification with field data, simulations of crop development models, and adding field data and simulations data to increase the accuracy of the model. Crop rotation mapping and crop rotation patterns for the state of RS were generated by enabling a more holistic look at the adoption of crop rotation strategies, intensification, and sustainability of agriculture to the state. The results presented in this study have the potential to contribute to digitization in agriculture, and may assist farmers, and policymakers during the decision-making process.A agricultura está sob intensa revolução, inúmeros dados são gerados a cada instante, seja pelo produtor, sensores, ou por geração de novos produtos para o setor agrícola, favorecendo a agricultura digital. Assim, os objetivos desse trabalho foram coletar dados de campo, bem como utilizar dados disponíveis como de sensoriamento remoto e dados geoespaciais de uso público para gerar conhecimento para a agricultura do Rio Grande do Sul (RS), Brasil. Os objetivos do primeiro trabalho foram i) avaliação da variabilidade espacial de dados de campo para gerar o modelo de classificação de culturas; ii) avaliar o modelo de transferência de aprendizagem com os dados da estação de cultivo subsequente; iii) avaliar a precisão do modelo de previsão para previsões antecipadas, e iv) desenvolver um modelo de classificação e mapeamento das culturas agrícolas para o RS. Os objetivos do segundo trabalho foram: i) comparar dados gerados através de simulações de desenvolvimento de cultivos agrícolas e dados de campo; ii) avaliar máscaras de campos de produção geradas pelo Cadastro Ambiental Rural, MapBiomas e modelo de random forest; e iii) avaliar modelos de classificação não-supervisionada, classificação supervisionada com dados de simulações de desenvolvimento de cultivos agrícolas, e classificação supervisionada com dados de campo, bem como a combinação dos mesmos. Os objetivos do terceiro trabalho foram: i) mapear padrões de monocultivos e rotação de culturas nas diferentes mesoregiões do estado do RS; ii) identificar variáveis de solo e de clima que coincide com maiores percentuais de área de monocultivo; iii) avaliar o efeito da rotação de culturas na produtividade de grãos das culturas. Como resultados dos trabalhos, foram gerados modelo de classificação e mapeamento de culturas agrícolas do RS, com a possibilidade de transferência de aprendizagem para safras subsequentes, obtendo predições a partir de 1º de janeiro da safra agrícola, aumentando a acurácia na medida que são capturadas mais imagens de sensoriamento remoto da safra. Também, no segundo trabalho foi possível gerar modelos de classificação de culturas agrícolas com diferentes modelos, classificação não-supervisionada, classificação supervisionada com dados de campo, com simulações de modelos de desenvolvimento de culturas, e agregando dados de campo e de simulações para aumentar a acurácia do modelo. O mapeamento de rotação de culturas e dos padrões de rotação de culturas para o estado do RS foram gerados possibilitando um olhar mais holístico para a adoção de estratégias de rotação de culturas, intensificação, e sustentabilidade da agricultura para o estado. Os resultados apresentados nesse estudo têm potencial para contribuir com a digitalização na agricultura, podendo auxiliar agricultores, e agentes formuladores de políticas durante o processo de tomada de decisão.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em AgronomiaUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAgricultura digitalMapeamento de culturas agrícolasModelos de culturas agrícolasMapeamento da rotação de culturasDigital agricultureCrop type mappingCrop modelsCrop rotation mappingCNPQ::CIENCIAS AGRARIAS::AGRONOMIAMapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do SulCrop mapping using remote sensing, crop modeling, and machine learning in Rio Grande do Sulinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisAmado, Telmo Jorge Carneirohttp://lattes.cnpq.br/8591926237097756Ciampitti, Ignacio AntonioSchwalbert, Raí AugustoCorassa, Geomar MateusBredemeier, Christianhttp://lattes.cnpq.br/6832541825379580Pott, Luan Pierre50010000000960060060060060060060040fa77fb-6780-43f4-937a-3f50982342af17ac20d1-5188-4786-8bd3-869d4e9e23685fd29195-5be5-48c8-b9f0-dc4193d70ae83144edb5-0025-4801-99a1-625725b1f912ae149e93-fd59-4342-b958-30576853502cb265521b-12fa-4b27-adbd-b5cd828d0fe5reponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGAGRONOMIA_2023_POTT_LUAN.pdfTES_PPGAGRONOMIA_2023_POTT_LUAN.pdfTeseapplication/pdf10135605http://repositorio.ufsm.br/bitstream/1/30420/1/TES_PPGAGRONOMIA_2023_POTT_LUAN.pdf8a2646fe46c3ee41913459b9b19026c6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
dc.title.alternative.eng.fl_str_mv |
Crop mapping using remote sensing, crop modeling, and machine learning in Rio Grande do Sul |
title |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
spellingShingle |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul Pott, Luan Pierre Agricultura digital Mapeamento de culturas agrícolas Modelos de culturas agrícolas Mapeamento da rotação de culturas Digital agriculture Crop type mapping Crop models Crop rotation mapping CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
title_full |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
title_fullStr |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
title_full_unstemmed |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
title_sort |
Mapeamento agrícola utilizando sensoriamento remoto, modelagem de culturas e aprendizado de máquina no Rio Grande do Sul |
author |
Pott, Luan Pierre |
author_facet |
Pott, Luan Pierre |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Amado, Telmo Jorge Carneiro |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/8591926237097756 |
dc.contributor.referee1.fl_str_mv |
Ciampitti, Ignacio Antonio |
dc.contributor.referee2.fl_str_mv |
Schwalbert, Raí Augusto |
dc.contributor.referee3.fl_str_mv |
Corassa, Geomar Mateus |
dc.contributor.referee4.fl_str_mv |
Bredemeier, Christian |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6832541825379580 |
dc.contributor.author.fl_str_mv |
Pott, Luan Pierre |
contributor_str_mv |
Amado, Telmo Jorge Carneiro Ciampitti, Ignacio Antonio Schwalbert, Raí Augusto Corassa, Geomar Mateus Bredemeier, Christian |
dc.subject.por.fl_str_mv |
Agricultura digital Mapeamento de culturas agrícolas Modelos de culturas agrícolas Mapeamento da rotação de culturas |
topic |
Agricultura digital Mapeamento de culturas agrícolas Modelos de culturas agrícolas Mapeamento da rotação de culturas Digital agriculture Crop type mapping Crop models Crop rotation mapping CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Digital agriculture Crop type mapping Crop models Crop rotation mapping |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
Agriculture is under intense revolution, numerous data is generated every moment, either by the farmers, sensors, or by generating new products for the agricultural sector, favoring digital agriculture. Thus, the objectives of this work were to collect field data, as well as use available data such as remote sensing and geospatial public data to generate knowledge for agriculture of Rio Grande do Sul (RS), Brazil. The objectives of the first study were to i) evaluate of the spatial variability of field data to generate the crop classification model; ii) evaluate the transfer learning model with the subsequent growing season data; iii) evaluate the accuracy of the forecast model for early forecasts, and IV) develop a classification and mapping model of agricultural crops for RS. The objectives of the second study were to: i) compare data generated through simulations of development of agricultural crops and field data; II) evaluate production fields masks generated by the Rural Environmental Registry, MapBiomas and random forest model; and iii) evaluate non-supervised classification models, supervised classification with data from agricultural crop development simulations, and supervised classification with field data, as well as their combination. The objectives of the third study were to: i) map monoculture patterns and crop rotation in the different mesoregions of the state of RS; ii) identify soil and climate variables that coincides with the highest percentages of monoculture area; iii) evaluate the effect of crop rotation on crop grain yields. As a result of the first study, the model of classification and mapping of agricultural crops of RS were generated, with the possibility of transfer learning to subsequent growing seasons, obtaining predictions from January 1 of the agricultural crop, increasing accuracy as more remote sensing images of the crops are captured. Also, in the second study it was possible to generate models of classification of crop types with different models, nonsupervisioned classification, supervised classification with field data, simulations of crop development models, and adding field data and simulations data to increase the accuracy of the model. Crop rotation mapping and crop rotation patterns for the state of RS were generated by enabling a more holistic look at the adoption of crop rotation strategies, intensification, and sustainability of agriculture to the state. The results presented in this study have the potential to contribute to digitization in agriculture, and may assist farmers, and policymakers during the decision-making process. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-11-01T11:50:13Z |
dc.date.available.fl_str_mv |
2023-11-01T11:50:13Z |
dc.date.issued.fl_str_mv |
2023-08-23 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/30420 |
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http://repositorio.ufsm.br/handle/1/30420 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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500100000009 |
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600 600 600 600 600 600 600 |
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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 Agronomia |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
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