Abstract
The effect of frost on the successful growth and quality of crops is well understood by growers as leading potentially to total harvest failure. Studying the frost phenomenon, especially in order to predict its occurrence has been the focus of numerous research projects and investigations. Frost prone areas are of particular concern. Grape growing for wine production is a specific area of viticulture and agricultural research. This paper describes the problem, outlines a wider project that is gathering climate and atmospheric data, together with soil, and plant data in order to determine the inter-dependencies of variable values that both inform enhanced crop management practices and where possible, predict optimal growing conditions. The application of some novel data mining techniques together with the use of computational neural networks as a means to modeling and then predicting frost is the focus of the investigation described here as part of the wider project.
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Sallis, P., Jarur, M., Trujillo, M. (2009). Frost Prediction Characteristics and Classification Using Computational Neural Networks. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_147
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DOI: https://doi.org/10.1007/978-3-642-02490-0_147
Publisher Name: Springer, Berlin, Heidelberg
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