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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
28/10/2014 |
Data da última atualização: |
08/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
SPERANZA, E. A.; CIFERRI, R. R.; GREGO, C. R.; VICENTE, L. E. |
Afiliação: |
EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO RODRIGUES CIFERRI, UFSCar; CÉLIA REGINA GREGO, CNPM; LUIZ EDUARDO VICENTE, CNPM. |
Título: |
A cluster-based approach to support the delineation of management zones in precision agriculture. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
In: IEEE INTERNATIONAL CONFERENCE ON E-SCIENCE, 10., 2014, Guarujá, SP. Conference proceedings. [S.l.]: Conferente Publishing Services, 2014. |
Páginas: |
p. 119-126. |
DOI: |
DOI 10.1109/eScience.2014.42 |
Idioma: |
Inglês |
Conteúdo: |
Abstract-In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed approach was built following the steps of data mining for the clustering task, resulting in a computer application that generates maps of management zones and yield areas, allowing to compare them using known statistical indexes. The basis for this implementation was a model previously published in the literature that uses only historical productivity, soil electrical conductivity and relief data to generate the maps. The main difference of our work with respect to the previous model is the clustering algorithms used in the step of extracting patterns. While the original model uses only the fuzzy c-means algorithm, the model developed in this study uses the GKCluster extension to this algorithm, able to detect clusters with different geometrical shapes. From the tests performed with the new proposed model, we achieved about 76% of correlation between maps of yield and management zones from kappa index, and about 85% of correlation from overall accuracy. The original model reached, according to the authors, a maximum correlation of 49% from kappa index, and 70% from overall accuracy. |
Palavras-Chave: |
Clusterização; Management zones; Mineração de dados espaciais; Spatial data mining. |
Thesagro: |
Agricultura de precisão. |
Thesaurus Nal: |
Cluster analysis; Precision agriculture. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 02132nam a2200253 a 4500 001 1998706 005 2020-01-08 008 2014 bl uuuu u00u1 u #d 024 7 $aDOI 10.1109/eScience.2014.42$2DOI 100 1 $aSPERANZA, E. A. 245 $aA cluster-based approach to support the delineation of management zones in precision agriculture.$h[electronic resource] 260 $aIn: IEEE INTERNATIONAL CONFERENCE ON E-SCIENCE, 10., 2014, Guarujá, SP. Conference proceedings. [S.l.]: Conferente Publishing Services$c2014 300 $ap. 119-126. 520 $aAbstract-In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed approach was built following the steps of data mining for the clustering task, resulting in a computer application that generates maps of management zones and yield areas, allowing to compare them using known statistical indexes. The basis for this implementation was a model previously published in the literature that uses only historical productivity, soil electrical conductivity and relief data to generate the maps. The main difference of our work with respect to the previous model is the clustering algorithms used in the step of extracting patterns. While the original model uses only the fuzzy c-means algorithm, the model developed in this study uses the GKCluster extension to this algorithm, able to detect clusters with different geometrical shapes. From the tests performed with the new proposed model, we achieved about 76% of correlation between maps of yield and management zones from kappa index, and about 85% of correlation from overall accuracy. The original model reached, according to the authors, a maximum correlation of 49% from kappa index, and 70% from overall accuracy. 650 $aCluster analysis 650 $aPrecision agriculture 650 $aAgricultura de precisão 653 $aClusterização 653 $aManagement zones 653 $aMineração de dados espaciais 653 $aSpatial data mining 700 1 $aCIFERRI, R. R. 700 1 $aGREGO, C. R. 700 1 $aVICENTE, L. E.
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Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
21/12/2015 |
Data da última atualização: |
21/12/2015 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
BERTONI, B. W.; PEREIRA, A. M. S.; PEREIRA, P. S.; DAMIÃO FILHO, C. F.; SALOMÃO, A. N.; FRANÇA, S. C.; MORAES, R. M.; CERDEIRA, A. L. |
Afiliação: |
B. W. BERTONI, Universidade de Ribeirão Preto; A. M. S. PEREIRA, Universidade de Ribeirão Preto; P. S. PEREIRA, Universidade de Ribeirão Preto; C. F. DAMIÃO FILHO, UNESP; A. N. SALOMÃO, Universidade de Ribeirão Preto; S. C. FRANÇA, Universidade de Ribeirão Preto; R. M. MORAES, NCNPR/University of Mississipi; ANTONIO LUIZ CERDEIRA, CNPMA. |
Título: |
Growth conditions of zeyheria montana mart as a source of lapachol. |
Ano de publicação: |
2006 |
Fonte/Imprenta: |
In: OXFORD INTERNATIONAL CONFERENCE ON THE SCIENCE OF BOTANICALS, 5., 2006, Oxford, Mississipi. Quality, safety and processing of botanical products. Oxford, Mississipi: The University of Mississippi, 2006. |
Idioma: |
Inglês |
Palavras-Chave: |
Espécie vegetal. |
Thesagro: |
Análise biológica. |
Categoria do assunto: |
W Química e Física |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/135982/1/2006RA-109.pdf
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Marc: |
LEADER 00778nam a2200205 a 4500 001 2032141 005 2015-12-21 008 2006 bl uuuu u00u1 u #d 100 1 $aBERTONI, B. W. 245 $aGrowth conditions of zeyheria montana mart as a source of lapachol.$h[electronic resource] 260 $aIn: OXFORD INTERNATIONAL CONFERENCE ON THE SCIENCE OF BOTANICALS, 5., 2006, Oxford, Mississipi. Quality, safety and processing of botanical products. Oxford, Mississipi: The University of Mississippi$c2006 650 $aAnálise biológica 653 $aEspécie vegetal 700 1 $aPEREIRA, A. M. S. 700 1 $aPEREIRA, P. S. 700 1 $aDAMIÃO FILHO, C. F. 700 1 $aSALOMÃO, A. N. 700 1 $aFRANÇA, S. C. 700 1 $aMORAES, R. M. 700 1 $aCERDEIRA, A. L.
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