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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
02/08/2017 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CAMARGO, L. de; OLIVEIRA, A. L. de; OLIVEIRA, S. R. de M.; COLETI, J. de C. |
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Afiliação: |
LARISSA DE CAMARGO, IAC; ANDRÉA LEDA RAMOS DE OLIVEIRA, Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; JAMILE DE CAMPOS COLETI, Unicamp. |
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Título: |
Analysis of regional patterns of grain production and storage in Brazil: a clustering application. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: CONFERENCE INTERNATIONAL CONSORTIUM ON APPLIED BIOECONOMY RESEARCH, 21., 2017, Berkeley. Bioeconomy in transition: new tools and new actors. Berkeley: University of California, 2017. |
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Idioma: |
Inglês |
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Conteúdo: |
The aim of this study is to identify patterns in the production and storage of grains in Brazil from 1980 to 2015 and analyze the conditions of similarity of each group. The technique of data mining Clustering was applied through the algorithm k-means to transform the historical series of grain storage and production into a group of similarities. The results show that the application of the mining technique coherently delimited the regions, which had a direct relation with the expansion of the agricultural frontier in Brazil, proving to be efficient for this application. Four clusters were formed. The first is composed by States of the South Region referring to consolidated crops since the 1960s. The second is composed of States of the Midwest and Southeast regions, revealing the second phase of the country's agricultural expansion. The third is made by states that do not have agricultural potential by soil and climate conditions, with an inexpressive agriculture. Finally, the fourth cluster shows the new Brazilian agricultural frontier called MATOPIBA (Maranhão, Tocantins, Piauí and Bahia), which has reached records in the production and export of grains driven by the installation of new transport routes. |
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Palavras-Chave: |
Data mining; Logistics. |
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Thesagro: |
Agronegócio. |
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Thesaurus Nal: |
agribusiness. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01955nam a2200193 a 4500
001 2073467
005 2020-01-07
008 2017 bl uuuu u00u1 u #d
100 1 $aCAMARGO, L. de
245 $aAnalysis of regional patterns of grain production and storage in Brazil$ba clustering application.$h[electronic resource]
260 $aIn: CONFERENCE INTERNATIONAL CONSORTIUM ON APPLIED BIOECONOMY RESEARCH, 21., 2017, Berkeley. Bioeconomy in transition: new tools and new actors. Berkeley: University of California$c2017
520 $aThe aim of this study is to identify patterns in the production and storage of grains in Brazil from 1980 to 2015 and analyze the conditions of similarity of each group. The technique of data mining Clustering was applied through the algorithm k-means to transform the historical series of grain storage and production into a group of similarities. The results show that the application of the mining technique coherently delimited the regions, which had a direct relation with the expansion of the agricultural frontier in Brazil, proving to be efficient for this application. Four clusters were formed. The first is composed by States of the South Region referring to consolidated crops since the 1960s. The second is composed of States of the Midwest and Southeast regions, revealing the second phase of the country's agricultural expansion. The third is made by states that do not have agricultural potential by soil and climate conditions, with an inexpressive agriculture. Finally, the fourth cluster shows the new Brazilian agricultural frontier called MATOPIBA (Maranhão, Tocantins, Piauí and Bahia), which has reached records in the production and export of grains driven by the installation of new transport routes.
650 $aagribusiness
650 $aAgronegócio
653 $aData mining
653 $aLogistics
700 1 $aOLIVEIRA, A. L. de
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aCOLETI, J. de C.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| 4. |  | SANTOS, T. T. Detecção automática de bagas de café em imagens de campo. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 10., 2015, Ponta Grossa. Uso de VANTs e sensores para avanços no agronegócio: anais. Ponta Grossa: Universidade Estadual de Ponta Grossa, 2015. Não paginado. SBIAgro 2015.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 5. | | SANTOS, T. T. Detecção de frutos por imagem com redes neurais convolutivas: um tutorial. In: BASSOI, L. H.; BERNARDI, A. C. de C.; VAZ, C. M. P.; PIRES, J. L. F.; GEBLER, L.; JORGE, L. A. de C.; INAMASU, R. Y. (ed.). Agricultura de precisão: um novo olhar na era digital. São Carlos, SP: Cubo, 2024. seção 1, cap. 10, p. 129-137.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 16. | | SANTOS, A. A. dos; SANTOS, T. T. Estudo de métodos de aprendizagem profunda para reconhecimento de bagas de uva. In: MOSTRA DE ESTAGIÁRIOS E BOLSISTAS DA EMBRAPA INFORMÁTICA AGROPCUÁRIA, 13., 2017, Campinas. Resumos expandidos... Brasília, DF: Embrapa, 2017. p. 43-46. Editores técnicos: Giampaolo Queiroz Pellegrino, Luciana Guilherme Sacomani Zenerato, Maria Fernanda Moura, Giulia Croce, Poliana Fernanda Giachetto.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 112 | |
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