Clear targets have been defined by the EU for a more competitive and sustainable agriculture (Green Deal). This requires data-driven decision making for farmers, governments and other policy makers, yet there is a severe reference-data gap when observations are needed at the local level. An underexploited source of data is generated by sensors used in agriculture, as they capture crucial information on the crops and the surrounding agri-environmental conditions. Tapping into this source and upscaling them the integration with other data (e.g. satellite) could result in enhanced capacities for regional agri-environmental monitoring. This would require a paradigm shift on how the monitoring systems work, and on the issues of data ownership and governance. The vision of ScaleAgData is thus to gain insight in (i) how these data streams should be governed to the benefit of all stakeholders, especially the farmers, and (ii) how these data can be integrated in the regional agri-environmental monitoring datasets. Through this upscaling, this wealth of information can be shared with a larger farmer community, thus shrinking the technological inequality in the sector. Specific attention will be paid to innovations in sensor technology, edge computing, data analytics, and novel EO-based products. These innovations will be co-designed and showcased in 6 Research and Innovation Labs, each with their specific thematic focus and spread across Europe. This will enable the assessment of the proposed innovations and data governance frameworks, and demonstrating added values of the improved monitoring capabilities for a range of users, including small-scale and agro-ecological farmers, the financial sector, and policy makers. With these outcomes, ScaleAgData aims at contributing to the overall competitiveness and sustainability performance of the European agricultural sector, and to the work of the HE candidate partnership “Agriculture of Data” and the Soil Mission.
In the context of ScaleAgData, ICCS is responsible for the design of the project’s wider architecture, the development of a system for data management as well as the coordination of the implementation tasks and the integration of the individual operating systems. Also, ICCS will contribute to the development of algorithms for the fusion and integration of primary data through the application of machine learning algorithms.
