Concept: To provide scientifically based information for use by grape growers and wine producers in their ongoing endeavour to improve crop production, product quality and yield through the synthesis of environmental and human sensory perception data.

The research relates to grape growing and wine production. This research is intended to be a long-term project observing climate, atmosphere, plant and soil data in vineyards, combined with data concerning wine quality gathered and analysed using a both expert and casual opinions. The methods used in the research are contemporary mathematical and software engineering methods and include novel techniques for how to select the 'rough data', how best to classify and model 'fuzzy data' and how best to synthesise precise or exact (quantitative) data, such as that from climatic and environmental attributes with other imprecise or inexact (qualitative) values. These qualitative values can be considered as fuzzy data which includes human sensory responses that form personal opinions based on smell, taste and other associated variables that form a spectrum of qualitative values. In the domain of wine tasting, these sensory variables are generally referred to as the 'structure' and 'complexity' of the finished product.

Arising from the proposition that wine is generally regarded as being of better quality in some production years than others, the mathematics and software engineering methods mentioned above are combined in this research with data processing techniques from the fields of spatial information processing, environmental and bio-ecological modelling, to develop a system that integrates all available data and synthesises them to produce scenarios that can be used to modify growing and production methods in order to achieve the best possible wine in any given year. Land-Satellite imagery will be used to provide a sequence of terrain maps showing changing weather patterns over time. Superimposed on these will be environmental data collected in real time from sample locations in Chile, Japan, Uruguay, Argentina, the USA and New Zealand to illustrate the atmospheric variability of the individual regions, which will provide a visual comparison in addition to the numeric correlations resulting from analysis of the data collected by telemetry devices feeding in real time to the GIS database, which can be interrogated for numerous cross-correlation purposes.

In some cases the data received will be in fuzzy form. Using contemporary neural network modelling software, these results will be collated and depicted in such a manner that scenarios for considering the 'best year' proposition can be appreciated and in conjunction with other factors, lead to the modification and optimisation of both growing and production methods. These neural networks will be analysed statistically to extract knowledge as to which variables have most effect on desired outcomes.

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