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Mining Climate and Remote Sensing Time Series to Improve Monitoring of Sugar Cane Fields
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Author(s): Luciana Romani (University of Sao Paulo at Sao Carlos, Brazil & Embrapa Agriculture Informatics at Campinas, Brazil), Elaine de Sousa (University of Sao Paulo at Sao Carlos, Brazil), Marcela Ribeiro (Federal University of Sao Carlos, Brazil), Ana de Ávila (University of Campinas, Brazil), Jurandir Zullo (University of Campinas, Brazil), Caetano Traina (University of Sao Paulo at Sao Carlos, Brazil)and Agma Traina (University of Sao Paulo at Sao Carlos, Brazil)
Copyright: 2013
Pages: 23
Source title:
Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch085
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Abstract
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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