AgroComputing.net: digital infrastructure and novel computational methods for analyzing and mining climate and remote sensing large databases to improve agricultural monitoring and forecasting
Abstract
This project aims at developing a computational platform to integrate climatic and remote sensor data obtained from several databases; and to propose computational methods to consist data, to fill absent data in the series, to identify new and useful patterns in order to improve the agricultural yield monitoring and forecasting models. The challenge for Computer Science comprehends the development of new algorithms to process, store, mine and analyze vast volumes of data (big data), as well as to propose a mechanism to provide autonomy for agricultural meteorologists to access data with parameterized query, to define new research needs, and to reformulate, intercompare and integrate agroenvironmental models. On the other hand, the scientific advance in Agrometeorology depends on a consistent, reliable and complete climatic database with spatial and temporal density (regular grades) for the whole country in order to generate models that can better support decisions in the agricultural business. Considering the climate change scenarios, the integration between computer scientists and agrometeorologists becomes essential, especially due to the increased amount of data generated by simulations of climate models, and from ground-based meteorological stations and remote sensors. In this context, improving computational methods for visual analytics, data mining, pattern recognition and visualization related to scientific workflow will allow upgrading models to analyze data in the current and future agroclimatic perspective. In an effort to better understand Climate Change and its impact on Agriculture, investigators of Embrapa Agricultural Informatics, ICMC-USP (São Carlos), Cepagri/UNICAMP, CPTEC/INPE, UFSCar, UFABC e UFU have been working together for several years, generating important contributions in both Computer Science and Agrometeorology fields. The validation of results will be done with economically and socially relevant agriculture crops in Brazil, such as sugar cane and coffee. Workshops and a virtual environment will be used to facilitate and support the integration, collaboration and communications among researchers in the project. (AU)
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