Data/hora: 09/06/2024 02:24:51

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	11/02/2011	
Data da última atualização: 	27/02/2024	
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.	
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.	
Título: 	Mining climate and remote sensing time series to improve monitoring of sugar cane fields.	
Ano de publicação: 	2011	
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.	
ISBN: 	978-1-61692-871-1	
DOI: 	10.4018/978-1-61692-871-1.ch004	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	Cana-de-açúcar; Dados climáticos; Modelos computacionais; Séries temporais.	
Thesagro: 	Sensoriamento Remoto.	
Thesaurus Nal: 	Models; Remote sensing; Sugarcane; Time series analysis.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (15579-1) - UPC - PL - DD - 630.72 - PRA - 2011.00299 - DIS