A system architecture for collaborative environmental modeling research

Abstract

This relates to early stage research that aims to build an integrated toolbox of instruments that can be used for environmental modeling tasks. The application area described is grape growing and wine production. A comparative study including data gathered in both New Zealand and Chile is described. Using both passive and sensor technology data is gathered from atmosphere, vines, and soil. Human sensory perceptions relating to wine taste and quality is also gathered. The project proposes a synthesizer which collects and analyzes data in real time. Computational neural network modeling methods and geographic information systems are used for result depiction. This convergence of computational techniques and information processing methods is proposed as being an example of software and systems collaboration. The project called Eno-Humanas is so named because of the bled of the precise enological data and less qualitative human perception data. It is expected that the discrete input elements of the architecture here will be demonstrably dependency-related and derived from correlation values once data gathering instruments and analytical software have been developed. At this stage of the project, these tools and methods are being built and tested. This is the first stage of the project and the proposed research that will come from it in order to answer wide questions such as the ordinal set of data values necessarily present to predict climate conditions, the relationship between vine sap rise and dew point calibrations, towards addressing the popular question of 'what makes for a good year for wine'. In addition to the bringing together of various technologies, methods and kinds of data, (geo-referential, climatic, atmospheric, terrain, plant biological and qualitative sensory expressions), the paper also describes an international research collaboration and its parameters.