Duration: April 2009 - December 2012
Funding: 14.200.000 EUR
Project Coordinator: Dr. Ansgar Bernardi
Technical Director: Christopher J. Tuot

The goal of the iGreen project is to realize a distributed network of location-based services and knowledge sources, i.e. enabling the integration of decentralized, highly heterogeneous public/private information sources. iGreen also aims to provide a framework for the development of mobile decision support systems. These systems use the iGreen network to support and optimize energy efficient, ecological, environ-mentally friendly collaborative production processes.

Possible domains of application that could benefit of the decision support processes provided by iGreen are for example agriculture, forestry, water management, land-scaping, environmental and nature conservation. Since crop production is mostly driven by spatial-temporal information, it was chosen as the first showcase to dem-onstrate how iGreen can enable mobile decision support systems to access highly heterogeneous information sources. For example, the system can use spatial infor-mation, such as public geo-data, domain specific knowledge and private user data to deliver real-time, on site, personal and efficient support.

Intelligent integration of source of information for business specific, location based planning for the production of energy crops (German: Intelligente Vernetzung verteilter Informationsquellen zur betriebs- und standortspezifischen Planung der Energiepflanzenerzeugung).

Duration: April 2007 - March 2008
Funding: 130.000 EUR
Project Coordinator: Dr. Ansgar Bernardi
Technical Director: Christopher J. Tuot

Decision making in crop production is essentially stamped by location-based or more precisely by space-oriented information or also by particular location driven situations. This is for example the case for the production of food and renewable raw-materials. Indeed, the cultivation of bio-raw materials can in certain regions lead to severe irregularities in the configuration of crop rotations. According to this fact, farmers and regional experts producing renewable raw materials mostly rely on new spatial-data-oriented decision making tools.

Within the RAPR project, DFKI conducted a feasibility study in Rhineland-Palatinate to show the benefits of a location-based biomass-planer using digitalized geographic information about ground allocation (FLOrlp) and soil quality (LGB). This project resulted from the successful cooperation of the different geologic and plant cultivation consulting facilities in Rhineland-Palatinate.

With the IVIP project, we intend to fill in the gap between the bid of information from LGB and the valuable location-based consulting services of DLR/ZEPP/ISIP in Bad Kreuznach by using the Web-based Spatial Decision Support System prototype developed in RAPR.

Location based Forecasts

Duration: April 2007 - December 2007
Funding: 109.000 EUR
Project Coordinator: Christopher J. Tuot

With the Location-Based Forecasts project, we intend to find new representation mechanisms allowing capture of all necessary factors that play a role in determining the best location for a POS. Those representation mechanisms should allow handling the necessary highly dynamic information including geographical information evolving in space and time. To demonstrate our results, we will consider Vending Machines as an example of use. Therefore, we will help SAP to develop a new Vending Simulator which will be used as demonstrator for our results.

Spatially Oriented Rule Based System for a Resource and Production Management of Raw Bio-Materials (German RAPR: Raumbezogene Regelwerkzeuge fuer ein Produktions- und Ressourcenmanagement von Biorohstoffen).

Duration: September 2005 - March 2007
Funding: 300.000 EUR
Project Coordinator: Dr. Ansgar Bernardi
Technical Director: Christopher J. Tuot

A DFKI Kaiserslautern - Knowledge Management - project for the region Rhineland-Palatinate in collaboration with John Deere and Agricultural Management Solutions (AMS) in Zweibruecken. RAPR aims to develop a rule-based prototype to process space-oriented agricultural knowledge in order to economically grow organic commodities. The prototype is planned to be tested while supporting the existing advisory service for production and resource management. After its completion the system will be available as open source software.