The three year Horizon Europe project STELAR has announced three piloting scenarios to get the agri-food data management project underway.

The project, which will run until September 2025 and is co-funded by the EU, is developing a data management system aimed to benefit many players on the food value chain and the environment.

In its previous period, STELAR members elaborated on the development of different business applications to be implemented.

It is now entering the implementation phase focusing on “customer satisfaction, software quality and environmental impact”.

The first pilot deals with data-driven methods for food safety in supply lines. It is led by the Greek data and analytics company Agroknow, which uses artificial intelligence (A.I) to predict risks and prevent food recalls.

The second pilot is headed by VISTA, an innovative German company in the field of remote sensing. The pilot is focused on early crop growth predictions based on earth observation data.

The third pilot considers data-driven decision-making in precision farming solutions.

It is led by the Italian company ABACO, a leader in development of software solutions for the management and control of land resources. Overall, these consist of activities grouped into 11 use cases.

Third-party users will be involved in all three piloting scenarios.

STELAR stated that the cases will be closely monitored in order to be evaluated in a timely manner and refined for the next stage of the project.

It aims to design, develop and evaluate a Knowledge Lake Management System (KLMS) to allow users to find, access and reuse data.

STELAR’s project coordinator and principal researcher at Athena Research Center, Dr. Dimitris Skoutas highlighted the importance of “tidying up” the data space for those involved in agri-food.

“Users in the agricultural sector are facing difficulties in meeting their needs due to data stored in different places and forms, as well as subpar dataset search capabilities underusing the revolutionising potential of AI and machine learning,” Dr. Dimitris Skoutas said.