| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 122 | |
| 10. |  | RABELLO, L. M.; INAMASU, R. Y.; SPERANZA, E. A. Complexo multi sensor de medida de dossel de planta. In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 4., 2019, São Carlos, SP. Ciência, inovação e mercado: anais. São Carlos, SP: Embrapa Instrumentação, 2019. p. 146-150. Editores: Paulino Ribeiro Villas-Boas, Maria Alice Martins, Débora Marcondes Bastos Pereira Milori, Ladislau Martin Neto. SIAGRO 2019.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
|   |
| 11. | | RABELLO, L. M.; INAMASU, R. Y.; SPERANZA, E. A. Complexo multi sensor de medida de dossel de planta. In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 4., 2019, São Carlos, SP. Ciência, inovação e mercado: anais. São Carlos, SP: Embrapa Instrumentação, 2019. Editores: Paulino Ribeiro Villas-Boas, Maria Alice Martins, Débora Marcondes Bastos Pereira Milori, Ladislau Martin Neto. SIAGRO 2019. 146-150| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Instrumentação. |
|  |
| Registros recuperados : 122 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
