Uso de dados geográficos e previsão automatizada com redes neurais na agricultura
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | https://tede.unioeste.br/handle/tede/6590 |
Resumo: | The world population demands an increased production of food for its subsistence. The search for processes and models that collaborate to increase productivity is a key factor to achieve results that minimize costs to the producer and contribute to decision making about the crop's needs. Geotechnologies, in conjunction with data analysis methods, allow monitoring of the productive area aiming to achieve better results. Thus, this work aims to present functionalities and methodologies that allow obtaining, processing, visualization, and prediction of indexes in agriculture in an automated way, not burdening the final user. In Article 01, it presents the application of tools in the generation of a database to be used by agriculture through an automation process for the implementation of a geographic database structure, using free software PostgreSQL+Postgis, which is fed by relevant data sources for agriculture (limits of the analyzed area and obtained by sensors). Thus, it results in a temporal data visualization tool that includes 16 indexes for analyzing the behavior of the productive area. In Article 02, from the consolidated base, Python language and convolutional neural networks were used to establish predictive models of NDVI indices, reaching mean absolute error (MAE) in the predictions from 0.16 to 0.17, with the presentation of better results for the use of networks that used windows of 5 images prior to the subsequent prediction. Both works made it possible to structure procedures for implementing, structuring, feeding, and analyzing agricultural databases in an automated way, minimizing costs and increasing the agility to generate relevant information for crop development. With this, the methodologies and tools used can be considered by producers or analysts to monitor, perform preventive actions and consequently achieve better results in agricultural production |
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Mercante, EriveltoPrudente, Victor Hugo RohdenHachisuca, Antonio Marcos MassaoBoas, Marcio Antonio VilasCoelho, Silvia Renata Machadohttp://lattes.cnpq.br/1968830390751686Wegner, Newmar2023-04-25T18:53:09Z2023-03-20Wegner, Newmar. Uso de dados geográficos e previsão automatizada com redes neurais na agricultura. 2023. 105 f. Tese(Doutorado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel.https://tede.unioeste.br/handle/tede/6590The world population demands an increased production of food for its subsistence. The search for processes and models that collaborate to increase productivity is a key factor to achieve results that minimize costs to the producer and contribute to decision making about the crop's needs. Geotechnologies, in conjunction with data analysis methods, allow monitoring of the productive area aiming to achieve better results. Thus, this work aims to present functionalities and methodologies that allow obtaining, processing, visualization, and prediction of indexes in agriculture in an automated way, not burdening the final user. In Article 01, it presents the application of tools in the generation of a database to be used by agriculture through an automation process for the implementation of a geographic database structure, using free software PostgreSQL+Postgis, which is fed by relevant data sources for agriculture (limits of the analyzed area and obtained by sensors). Thus, it results in a temporal data visualization tool that includes 16 indexes for analyzing the behavior of the productive area. In Article 02, from the consolidated base, Python language and convolutional neural networks were used to establish predictive models of NDVI indices, reaching mean absolute error (MAE) in the predictions from 0.16 to 0.17, with the presentation of better results for the use of networks that used windows of 5 images prior to the subsequent prediction. Both works made it possible to structure procedures for implementing, structuring, feeding, and analyzing agricultural databases in an automated way, minimizing costs and increasing the agility to generate relevant information for crop development. With this, the methodologies and tools used can be considered by producers or analysts to monitor, perform preventive actions and consequently achieve better results in agricultural productionA população mundial demanda uma crescente produção de alimentos para subsistência. A busca por processos e modelos que colaborem no aumento de produtividade é fator primordial para alcançar resultados que minimizem custos ao produtor e que contribuam na tomada de decisões sobre as necessidades da lavoura. As geotecnologias, em conjunto com métodos de análises de dados, permitem monitorar a área produtiva visando alcançar melhores resultados. Assim, este trabalho tem por objetivo apresentar funcionalidades e metodologias que permitam a obtenção, processamento, visualização e previsão de índices na agricultura, de forma automatizada, não onerando o usuário final. Em seu Artigo 01, apresenta a aplicação de ferramentas na geração de base de dados para utilização pela agricultura por meio de um processo de automatização para implantação da estrutura de um banco de dados geográfico, utilizando software livre PostgreSQL+Postgis, que é alimentado por fontes de dados relevantes para a agricultura (limite da área analisada e obtidos por sensores); assim, resulta em uma ferramenta temporal de visualização de dados que contempla um total de 16 índices para análise do comportamento da área produtiva. No Artigo 02, a partir da base consolidada, utilizou-se linguagem python e redes neurais convolucionais para estabelecimento de modelos preditivos de índices NDVI, alcançando erro médio absoluto (MAE) nas previsões de 0.16 a 0.17, com apresentação de melhores resultados para o uso de redes que utilizavam janelas de 5 imagens anteriores à previsão subsequente. Ambos os trabalhos possibilitaram estruturar procedimentos de implantação, estruturação, alimentação e análise de base de dados para agricultura de forma automatizada, minimizando custos e aumentando a agilidade da geração de informações relevantes ao desenvolvimento de culturas. Com isso, as metodologias e ferramentas utilizadas podem ser consideradas por produtores ou analistas, de forma a monitorar, realizar ações preventivas e consequentemente alcançar melhores resultados na produção agrícola.Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2023-04-25T18:53:09Z No. of bitstreams: 2 NEWMAR _WEGNER.2023.pdf: 4353635 bytes, checksum: bff7e17dc9b3919477883e402cefbc91 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2023-04-25T18:53:09Z (GMT). No. of bitstreams: 2 NEWMAR _WEGNER.2023.pdf: 4353635 bytes, checksum: bff7e17dc9b3919477883e402cefbc91 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2023-03-20application/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Engenharia AgrícolaUNIOESTEBrasilCentro de Ciências Exatas e Tecnológicashttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAgricultura de precisãoPostgreSQLPythonSensoriamento remotoPrecision farmingPostgreSQLPythonRemote sensingSISTEMAS BIOLÓGICOS E AGROINDUSTRIAISUso de dados geográficos e previsão automatizada com redes neurais na agriculturaUse of geographic data and automated prediction with neural networks in agricultureinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-53476924504160521296006002214374442868382015reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALNEWMAR _WEGNER.2023.pdfNEWMAR _WEGNER.2023.pdfapplication/pdf4353635http://tede.unioeste.br:8080/tede/bitstream/tede/6590/5/NEWMAR+_WEGNER.2023.pdfbff7e17dc9b3919477883e402cefbc91MD55CC-LICENSElicense_urllicense_urltext/plain; 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dc.title.por.fl_str_mv |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
dc.title.alternative.eng.fl_str_mv |
Use of geographic data and automated prediction with neural networks in agriculture |
title |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
spellingShingle |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura Wegner, Newmar Agricultura de precisão PostgreSQL Python Sensoriamento remoto Precision farming PostgreSQL Python Remote sensing SISTEMAS BIOLÓGICOS E AGROINDUSTRIAIS |
title_short |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
title_full |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
title_fullStr |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
title_full_unstemmed |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
title_sort |
Uso de dados geográficos e previsão automatizada com redes neurais na agricultura |
author |
Wegner, Newmar |
author_facet |
Wegner, Newmar |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Mercante, Erivelto |
dc.contributor.referee1.fl_str_mv |
Prudente, Victor Hugo Rohden |
dc.contributor.referee2.fl_str_mv |
Hachisuca, Antonio Marcos Massao |
dc.contributor.referee3.fl_str_mv |
Boas, Marcio Antonio Vilas |
dc.contributor.referee4.fl_str_mv |
Coelho, Silvia Renata Machado |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1968830390751686 |
dc.contributor.author.fl_str_mv |
Wegner, Newmar |
contributor_str_mv |
Mercante, Erivelto Prudente, Victor Hugo Rohden Hachisuca, Antonio Marcos Massao Boas, Marcio Antonio Vilas Coelho, Silvia Renata Machado |
dc.subject.por.fl_str_mv |
Agricultura de precisão PostgreSQL Python Sensoriamento remoto |
topic |
Agricultura de precisão PostgreSQL Python Sensoriamento remoto Precision farming PostgreSQL Python Remote sensing SISTEMAS BIOLÓGICOS E AGROINDUSTRIAIS |
dc.subject.eng.fl_str_mv |
Precision farming PostgreSQL Python Remote sensing |
dc.subject.cnpq.fl_str_mv |
SISTEMAS BIOLÓGICOS E AGROINDUSTRIAIS |
description |
The world population demands an increased production of food for its subsistence. The search for processes and models that collaborate to increase productivity is a key factor to achieve results that minimize costs to the producer and contribute to decision making about the crop's needs. Geotechnologies, in conjunction with data analysis methods, allow monitoring of the productive area aiming to achieve better results. Thus, this work aims to present functionalities and methodologies that allow obtaining, processing, visualization, and prediction of indexes in agriculture in an automated way, not burdening the final user. In Article 01, it presents the application of tools in the generation of a database to be used by agriculture through an automation process for the implementation of a geographic database structure, using free software PostgreSQL+Postgis, which is fed by relevant data sources for agriculture (limits of the analyzed area and obtained by sensors). Thus, it results in a temporal data visualization tool that includes 16 indexes for analyzing the behavior of the productive area. In Article 02, from the consolidated base, Python language and convolutional neural networks were used to establish predictive models of NDVI indices, reaching mean absolute error (MAE) in the predictions from 0.16 to 0.17, with the presentation of better results for the use of networks that used windows of 5 images prior to the subsequent prediction. Both works made it possible to structure procedures for implementing, structuring, feeding, and analyzing agricultural databases in an automated way, minimizing costs and increasing the agility to generate relevant information for crop development. With this, the methodologies and tools used can be considered by producers or analysts to monitor, perform preventive actions and consequently achieve better results in agricultural production |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-04-25T18:53:09Z |
dc.date.issued.fl_str_mv |
2023-03-20 |
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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dc.identifier.citation.fl_str_mv |
Wegner, Newmar. Uso de dados geográficos e previsão automatizada com redes neurais na agricultura. 2023. 105 f. Tese(Doutorado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel. |
dc.identifier.uri.fl_str_mv |
https://tede.unioeste.br/handle/tede/6590 |
identifier_str_mv |
Wegner, Newmar. Uso de dados geográficos e previsão automatizada com redes neurais na agricultura. 2023. 105 f. Tese(Doutorado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel. |
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https://tede.unioeste.br/handle/tede/6590 |
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por |
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2214374442868382015 |
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openAccess |
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Universidade Estadual do Oeste do Paraná Cascavel |
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Programa de Pós-Graduação em Engenharia Agrícola |
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UNIOESTE |
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Centro de Ciências Exatas e Tecnológicas |
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Universidade Estadual do Oeste do Paraná Cascavel |
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