EXPLORE THE DATA ECOSYSTEM

The aim of the MoDeM_IVM project is to develop a new, interactive, web-based Decision Support System (DSS) for integrated management of the vineyard. The SME participants are strongly confident that the project results will improve their competitiveness in a very important and highly profitable market (i.e., grapevine in EU27), because of: i) the DSS is highly innovative and focused to solve practical problems; ii) end-users will draw clear economic and environmental advantages; iii) the EU policies will determine a huge increase of the Internet use in agricultural areas in coming years.

The project has 8 partners (3 SMEs and 5 RTD performers) settled in four EU countries; other countries and end-users are involved in some project activities. The project has 8 Work Packages. RTD is mainly in charge to RTD performers but involves also the SMEs. Research is mainly addressed to: i) develop and integrate in a single system automatic sensors and hand-held devices for monitoring all the vineyard components (WP1); ii) develop mathematical models for the key aspects of the vineyard management (WP2); iii) define the best options for managing the vineyard according to the Integrated Production and bring these options into practical guidelines (WP2); iv) optimise decision making based on a cost-benefit analysis that also considers environmental impacts (WP3); v) develop the web-based DSS that: receives real-time input data from the vineyard; uses data for calculating optimised decision supports; shows the decision supports in a clear way (WP4). Dissemination activities (WP5) will be targeted to end users (validation of the DSS in commercial vineyards and seminars), and to the scientific world (international congress, publications and a web-site). Training for the SME staff is aimed at facilitating the take up of results (WP6). Management of the project activities, knowledge, IPR and exploitation of the results by the SMEs have a specific WP (WP7).

Acronym
MoDeM_IVM
Website
Address
IT
Scientific discipline(s)
Geographic coverage
Type
Data science categories
Data science categories