EFRA (Extreme Food Risk Analytics)
A new EU project to transform food risk prediction with AI-powered analytics.
Before they happen: EFRA to take advantage of extreme data management technologies with a ground-breaking potential for forecasting, identifying and preventing emerging food risks.
Well, the consortium of partners for the EFRA project has a different opinion. EFRA’s initiative is to beat time by utilizing new frontiers in data-driven decision-making. For the initiation of this ambitious project, a kick-off meeting took place in early February in Athens and had the project off to a great start. This European Union’s co-funded project will showcase a high-tech paradigm shift with a tremendous potential impact on ensuring safer and more sustainable food in a global environment that is under enormous pressure. All 9 partners -coming from 7 different European countries- have been brought together, ready to unite their expertise and efforts for the next 3 years.
Before they happen
Through digitization and developments in sensor networks and Internet of Things connectivity, the collection of data along the food supply chain has increased to a huge scale. However, this public and private data is characterized by increasing volumes, with extreme variety and velocity coupled with significant diversity and complexity, in many formats, types and languages, from data sources dispersed around the world, with missing values and insufficient contexts.
EFRA’S core ambition is to overcome these boundaries by exploring novel, experimental and promising approaches in extreme data mining, aggregation and analytics technologies. The first step of the project is to recognize and collect this wealth of heterogeneous data scattered throughout the internet, convert it into a “universal language” of high-quality risk food data able to train an AI model to proactively provide risk mitigation measures (based on predictive awareness of short- and long-term risks) with an explainable, secure, sensitive, accurate, trustworthy, fair and green manner, before food risks of contamination, quality or even fraud-happen.