Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/6336 |
Resumo: | Precision agriculture can be defined as a set of techniques and technologies that can be implemented to improve the decision-making process in agricultural production, as it allows the precise application of fertilizers at each location. As agricultural areas are usually not homogeneous, one of the options to deal with the heterogeneity of the soil or the distribution of chemical and physical attributes is to define application zones. The application zones make it possible to reduce both the spatial and temporal variability of the crop yield under study as well as the environmental impacts. Therefore, the application zones can also represent indicators to guide future soil sampling, aiming at a possible reduction in the sample size. The objective of this work was to determine a better sample resizing (with traditional sampling – Article 1; and with optimization process – Article 2) for a commercial area of soybean cultivation, where an activity of localized application of agricultural inputs is developed, through zones of application generated from the evaluation of five clustering methods (Fuzzy C-means, Fanny, K-means, Mcquitty, and Ward). Soil chemical attributes obtained from an agricultural area located in the municipality of Cascavel, PR, Brazil, referring to four years of soybean harvest (2013-2014; 2014-2015; 2015-2016; and 2016-2017) were used. Initially, a descriptive and geostatistical analysis of the chemical attributes of the soil was carried out. Subsequently, the application zones were obtained through clustering methods considering the dissimilarity matrix that aggregates information about the Euclidean distance between the sample elements and the spatial dependence structure of the attributes. Subsequently, reduced sample configurations were obtained with 50 and 75% of the initial sample points in these application zones. Afterwards, the descriptive and geostatistical analyzes of the reduced sample configurations were performed again. Finally, the sample configurations (initial and reduced) were compared, by means of the measure of similarity Global Accuracy and the Kappa and Tau concordance indices, in order to determine which configuration provided a better estimation of the variable in unsampled locations. For the crop years under study, the K-means and Ward clustering methods were efficient in defining the application zones, dividing the study area into two or three application zones. Comparing all the reduced sample configurations with the initial one, it was observed that the configuration proportionally reduce and optimized by 25% (selecting 75% of the initial configuration points, which corresponds to 76 sample points) were the most effective in terms of accuracy indices (global accuracy, Kappa, Tau), indicating greater similarity between the thematic maps of these sample configurations. Thus, the reduced sample configurations could be used to generate the application zones, as well as reduce the costs with laboratory analyzes involved in the study. |
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Guedes, Luciana Pagliosa Carvalhohttp://lattes.cnpq.br/3195220544719864Opazo, Miguel Angel Uribehttp://lattes.cnpq.br/4179444121729414Guedes, Luciana Pagliosa Carvalhohttp://lattes.cnpq.br/3195220544719864Opazo, Miguel Angel Uribehttp://lattes.cnpq.br/4179444121729414Villwock, Rosangelahttp://lattes.cnpq.br/2576133417405952Dalposo, Gustavo Henriquehttp://lattes.cnpq.br/8040071176709565Gavioli, Alanhttp://lattes.cnpq.br/3689948487608659http://lattes.cnpq.br/1464108924371037Maltauro, Tamara Cantú2022-12-08T13:05:21Z2022-09-01MALTAURO, Tamara Cantú. Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial. 2022. 128 f. Tese (Doutorado - Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, PR.https://tede.unioeste.br/handle/tede/6336Precision agriculture can be defined as a set of techniques and technologies that can be implemented to improve the decision-making process in agricultural production, as it allows the precise application of fertilizers at each location. As agricultural areas are usually not homogeneous, one of the options to deal with the heterogeneity of the soil or the distribution of chemical and physical attributes is to define application zones. The application zones make it possible to reduce both the spatial and temporal variability of the crop yield under study as well as the environmental impacts. Therefore, the application zones can also represent indicators to guide future soil sampling, aiming at a possible reduction in the sample size. The objective of this work was to determine a better sample resizing (with traditional sampling – Article 1; and with optimization process – Article 2) for a commercial area of soybean cultivation, where an activity of localized application of agricultural inputs is developed, through zones of application generated from the evaluation of five clustering methods (Fuzzy C-means, Fanny, K-means, Mcquitty, and Ward). Soil chemical attributes obtained from an agricultural area located in the municipality of Cascavel, PR, Brazil, referring to four years of soybean harvest (2013-2014; 2014-2015; 2015-2016; and 2016-2017) were used. Initially, a descriptive and geostatistical analysis of the chemical attributes of the soil was carried out. Subsequently, the application zones were obtained through clustering methods considering the dissimilarity matrix that aggregates information about the Euclidean distance between the sample elements and the spatial dependence structure of the attributes. Subsequently, reduced sample configurations were obtained with 50 and 75% of the initial sample points in these application zones. Afterwards, the descriptive and geostatistical analyzes of the reduced sample configurations were performed again. Finally, the sample configurations (initial and reduced) were compared, by means of the measure of similarity Global Accuracy and the Kappa and Tau concordance indices, in order to determine which configuration provided a better estimation of the variable in unsampled locations. For the crop years under study, the K-means and Ward clustering methods were efficient in defining the application zones, dividing the study area into two or three application zones. Comparing all the reduced sample configurations with the initial one, it was observed that the configuration proportionally reduce and optimized by 25% (selecting 75% of the initial configuration points, which corresponds to 76 sample points) were the most effective in terms of accuracy indices (global accuracy, Kappa, Tau), indicating greater similarity between the thematic maps of these sample configurations. Thus, the reduced sample configurations could be used to generate the application zones, as well as reduce the costs with laboratory analyzes involved in the study.A agricultura de precisão pode ser definida como um conjunto de técnicas e tecnologias que podem ser empregadas para melhorar o processo de tomada de decisão na produção agrícola, pois permite a aplicação específica de fertilizantes em cada local. Como as áreas agrícolas normalmente não são homogêneas, uma das propostas para lidar com a heterogeneidade do solo ou da distribuição dos atributos químicos e físicos é definir as zonas de aplicação. As zonas de aplicação permitem reduzir tanto a variabilidade espacial e temporal do rendimento da cultura em estudo como os impactos ambientais. Sendo assim, as zonas de aplicação também podem representar indicadores para direcionar futuras amostragens de solo, visando uma possível redução do tamanho da amostra. O objetivo deste trabalho foi determinar um melhor redimensionamento amostral (com amostragens tradicionais – Artigo 1; e com processo de otimização – Artigo 2) para uma área comercial de cultivo de soja onde se desenvolve uma atividade de aplicação localizada de insumos, por meio de zonas de aplicação geradas a partir da avaliação de cinco métodos de agrupamento (Fuzzy C-means, Fanny, K-means, Mcquitty e Ward). Utilizaram-se atributos químicos do solo obtidos de uma área agrícola localizada no município de Cascavel, PR, referente a quatro anos de safra de soja (2013-2014; 2014-2015; 2015-2016; e 2016-2017). Inicialmente, realizou-se a análise descritiva e geoestatística dos atributos químicos do solo. Na sequência, as zonas de aplicação foram obtidas por meio dos métodos de agrupamento considerando a matriz de dissimilaridade que agrega informações sobre a distância euclidiana entre os elementos amostrais e a estrutura de dependência espacial dos atributos. Posteriormente, foram obtidas as configurações amostrais reduzidas com 50 e 75% dos pontos amostrais iniciais nessas zonas de aplicação. Então, realizaram-se novamente as análises descritivas e geoestatísticas das configurações amostrais reduzidas. Por fim, comparou-se por meio da medida de similaridade Exatidão Global e os índices de concordância Kappa e Tau qual configuração amostral (inicial ou reduzida) forneceu uma melhor estimação da variável em localizações não amostradas. Para os anos de safra em estudo, os métodos de agrupamento K-means e Ward foram eficientes na definição das zonas de aplicação, dividindo a área de estudo em duas ou três zonas de aplicação. Comparando todas as configurações amostrais reduzidas com a inicial, observou-se que a reduzida proporcionalmente e otimizadas em 25% (selecionando 75% dos pontos de configuração inicial, o que corresponde a 76 pontos amostrais) foram as mais eficazes em termos de índices de precisão (exatidão global, Kappa, Tau), indicando maior similaridade entre os mapas temáticos dessas configurações amostrais. Dessa forma, as configurações amostrais reduzidas poderiam ser utilizadas para gerar as zonas de aplicação, bem como reduzir os custos com as análises laboratoriais envolvidas no estudo.Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2022-12-08T13:05:21Z No. of bitstreams: 2 Tamara Cantú Maltauro.pdf: 6140828 bytes, checksum: 62bdb55095321de8942d34a124ae84be (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2022-12-08T13:05:21Z (GMT). No. of bitstreams: 2 Tamara Cantú Maltauro.pdf: 6140828 bytes, checksum: 62bdb55095321de8942d34a124ae84be (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2022-09-01Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Engenharia AgrícolaUNIOESTEBrasilCentro de Ciências Exatas e Tecnológicashttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAgricultura de precisãoAlgoritmo genéticoConfiguração amostralGeoestatísticaMatriz de dissimilaridade espacialRedução amostralPrecision agricultureGenetic algorithmSample configurationGeostatisticsSpatial dissimilarity matrixSample reductioCIENCIAS AGRARIAS::ENGENHARIA AGRICOLAEstudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacialStudy of multivariate optimization algorithms for the determination of sample configuration and sample size in the analysis of spatial variabilityinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-5347692450416052129600600600600221437444286838201591854457215887615552075167498588264571reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALTamara Cantú Maltauro.pdfTamara Cantú Maltauro.pdfapplication/pdf6140828http://tede.unioeste.br:8080/tede/bitstream/tede/6336/5/Tamara+Cant%C3%BA+Maltauro.pdf62bdb55095321de8942d34a124ae84beMD55CC-LICENSElicense_urllicense_urltext/plain; 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dc.title.por.fl_str_mv |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
dc.title.alternative.eng.fl_str_mv |
Study of multivariate optimization algorithms for the determination of sample configuration and sample size in the analysis of spatial variability |
title |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
spellingShingle |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial Maltauro, Tamara Cantú Agricultura de precisão Algoritmo genético Configuração amostral Geoestatística Matriz de dissimilaridade espacial Redução amostral Precision agriculture Genetic algorithm Sample configuration Geostatistics Spatial dissimilarity matrix Sample reductio CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
title_short |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
title_full |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
title_fullStr |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
title_full_unstemmed |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
title_sort |
Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial |
author |
Maltauro, Tamara Cantú |
author_facet |
Maltauro, Tamara Cantú |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Guedes, Luciana Pagliosa Carvalho |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3195220544719864 |
dc.contributor.advisor-co1.fl_str_mv |
Opazo, Miguel Angel Uribe |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/4179444121729414 |
dc.contributor.referee1.fl_str_mv |
Guedes, Luciana Pagliosa Carvalho |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/3195220544719864 |
dc.contributor.referee2.fl_str_mv |
Opazo, Miguel Angel Uribe |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/4179444121729414 |
dc.contributor.referee3.fl_str_mv |
Villwock, Rosangela |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/2576133417405952 |
dc.contributor.referee4.fl_str_mv |
Dalposo, Gustavo Henrique |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/8040071176709565 |
dc.contributor.referee5.fl_str_mv |
Gavioli, Alan |
dc.contributor.referee5Lattes.fl_str_mv |
http://lattes.cnpq.br/3689948487608659 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1464108924371037 |
dc.contributor.author.fl_str_mv |
Maltauro, Tamara Cantú |
contributor_str_mv |
Guedes, Luciana Pagliosa Carvalho Opazo, Miguel Angel Uribe Guedes, Luciana Pagliosa Carvalho Opazo, Miguel Angel Uribe Villwock, Rosangela Dalposo, Gustavo Henrique Gavioli, Alan |
dc.subject.por.fl_str_mv |
Agricultura de precisão Algoritmo genético Configuração amostral Geoestatística Matriz de dissimilaridade espacial Redução amostral |
topic |
Agricultura de precisão Algoritmo genético Configuração amostral Geoestatística Matriz de dissimilaridade espacial Redução amostral Precision agriculture Genetic algorithm Sample configuration Geostatistics Spatial dissimilarity matrix Sample reductio CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
dc.subject.eng.fl_str_mv |
Precision agriculture Genetic algorithm Sample configuration Geostatistics Spatial dissimilarity matrix Sample reductio |
dc.subject.cnpq.fl_str_mv |
CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
description |
Precision agriculture can be defined as a set of techniques and technologies that can be implemented to improve the decision-making process in agricultural production, as it allows the precise application of fertilizers at each location. As agricultural areas are usually not homogeneous, one of the options to deal with the heterogeneity of the soil or the distribution of chemical and physical attributes is to define application zones. The application zones make it possible to reduce both the spatial and temporal variability of the crop yield under study as well as the environmental impacts. Therefore, the application zones can also represent indicators to guide future soil sampling, aiming at a possible reduction in the sample size. The objective of this work was to determine a better sample resizing (with traditional sampling – Article 1; and with optimization process – Article 2) for a commercial area of soybean cultivation, where an activity of localized application of agricultural inputs is developed, through zones of application generated from the evaluation of five clustering methods (Fuzzy C-means, Fanny, K-means, Mcquitty, and Ward). Soil chemical attributes obtained from an agricultural area located in the municipality of Cascavel, PR, Brazil, referring to four years of soybean harvest (2013-2014; 2014-2015; 2015-2016; and 2016-2017) were used. Initially, a descriptive and geostatistical analysis of the chemical attributes of the soil was carried out. Subsequently, the application zones were obtained through clustering methods considering the dissimilarity matrix that aggregates information about the Euclidean distance between the sample elements and the spatial dependence structure of the attributes. Subsequently, reduced sample configurations were obtained with 50 and 75% of the initial sample points in these application zones. Afterwards, the descriptive and geostatistical analyzes of the reduced sample configurations were performed again. Finally, the sample configurations (initial and reduced) were compared, by means of the measure of similarity Global Accuracy and the Kappa and Tau concordance indices, in order to determine which configuration provided a better estimation of the variable in unsampled locations. For the crop years under study, the K-means and Ward clustering methods were efficient in defining the application zones, dividing the study area into two or three application zones. Comparing all the reduced sample configurations with the initial one, it was observed that the configuration proportionally reduce and optimized by 25% (selecting 75% of the initial configuration points, which corresponds to 76 sample points) were the most effective in terms of accuracy indices (global accuracy, Kappa, Tau), indicating greater similarity between the thematic maps of these sample configurations. Thus, the reduced sample configurations could be used to generate the application zones, as well as reduce the costs with laboratory analyzes involved in the study. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-12-08T13:05:21Z |
dc.date.issued.fl_str_mv |
2022-09-01 |
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.citation.fl_str_mv |
MALTAURO, Tamara Cantú. Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial. 2022. 128 f. Tese (Doutorado - Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, PR. |
dc.identifier.uri.fl_str_mv |
https://tede.unioeste.br/handle/tede/6336 |
identifier_str_mv |
MALTAURO, Tamara Cantú. Estudo de algoritmos de otimização multivariados para a determinação de configuração amostral e tamanho amostral na análise da variabilidade espacial. 2022. 128 f. Tese (Doutorado - Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, PR. |
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https://tede.unioeste.br/handle/tede/6336 |
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600 600 600 600 |
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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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Brasil |
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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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