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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
11/02/2011 |
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Data da última atualização: |
27/02/2024 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
ROMANI, L. A. S.; SOUSA, E. P. M. de; RIBEIRO, M. X.; ÁVILA, A. M. H. de; ZULLO JÚNIOR, J.; TRAINA JÚNIOR, C.; TRAINA, A. J. M. |
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Afiliação: |
LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, USP; MARCELA X. RIBEIRO, USP; ANA M. H. de ÁVILA, UNICAMP; JURANDIR ZULLO JÚNIOR, UNICAMP; CAETANO TRAINA JÚNIOR, USP; AGMA J. M. TRAINA, USP. |
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Título: |
Mining climate and remote sensing time series to improve monitoring of sugar cane fields. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: PRADO, H. A. do; LUIZ, A. J. B.; CHAIB FILHO, H. Computational Methods for Agricultural Research: Advances and Applications. Hershey: Information Science Reference, 2011. chap. 4, p. 50-72. |
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ISBN: |
978-1-61692-871-1 |
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DOI: |
10.4018/978-1-61692-871-1.ch004 |
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Idioma: |
Inglês |
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Conteúdo: |
This chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and remote sensing images. This kind of data has been used for researching on climate changes, as well as to help on improving yield forecasting of agricultural crops and increasing the sustainable usage of the soil. The authors present three techniques based on the Fractal Theory, data streams and time series mining: the FDASE algorithm, to identify correlated attributes; a method that combines intrinsic dimension measurements with statistical analysis, to monitor evolving climate and remote sensing data; and the CLIPSMiner algorithm applied to multiple time series of continuous climate data, to identify relevant and extreme patterns. The experiments with real data show that data mining is a valuable tool to help agricultural entrepreneurs and government on monitoring sugar cane areas, helping to make the production more useful to the country and to the environment. |
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Palavras-Chave: |
Cana-de-açúcar; Dados climáticos; Modelos computacionais; Séries temporais. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus NAL: |
Models; Remote sensing; Sugarcane; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02140naa a2200325 a 4500
001 1876738
005 2024-02-27
008 2011 bl uuuu u00u1 u #d
020 $a978-1-61692-871-1
024 7 $a10.4018/978-1-61692-871-1.ch004$2DOI
100 1 $aROMANI, L. A. S.
245 $aMining climate and remote sensing time series to improve monitoring of sugar cane fields.$h[electronic resource]
260 $c2011
520 $aThis chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and remote sensing images. This kind of data has been used for researching on climate changes, as well as to help on improving yield forecasting of agricultural crops and increasing the sustainable usage of the soil. The authors present three techniques based on the Fractal Theory, data streams and time series mining: the FDASE algorithm, to identify correlated attributes; a method that combines intrinsic dimension measurements with statistical analysis, to monitor evolving climate and remote sensing data; and the CLIPSMiner algorithm applied to multiple time series of continuous climate data, to identify relevant and extreme patterns. The experiments with real data show that data mining is a valuable tool to help agricultural entrepreneurs and government on monitoring sugar cane areas, helping to make the production more useful to the country and to the environment.
650 $aModels
650 $aRemote sensing
650 $aSugarcane
650 $aTime series analysis
650 $aSensoriamento Remoto
653 $aCana-de-açúcar
653 $aDados climáticos
653 $aModelos computacionais
653 $aSéries temporais
700 1 $aSOUSA, E. P. M. de
700 1 $aRIBEIRO, M. X.
700 1 $aÁVILA, A. M. H. de
700 1 $aZULLO JÚNIOR, J.
700 1 $aTRAINA JÚNIOR, C.
700 1 $aTRAINA, A. J. M.
773 $tIn: PRADO, H. A. do; LUIZ, A. J. B.; CHAIB FILHO, H. Computational Methods for Agricultural Research: Advances and Applications. Hershey: Information Science Reference, 2011. chap. 4, p. 50-72.
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