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The first operational data and analytics platform dedicated to food safety risk prevention in Europe.
Mission
EFRA aspires to develop the first analytics-enabled, secure-by-design, green data space for AI-enabled food risk prevention. Our mission is to support EU’s global leadership in the digital-led industry transition from reaction to food risk prevention.
EFRA Project
Objectives
1 Solution development and testing, able to discover and distil meaningful and reliable food risk data from heterogeneous and dispersed data sources with minimal delay and appropriate format.
2 Designing of relevant human aspects and interactions with users to measure usefulness for human risk prevention actions in real-world scenarios and use-cases
3 Demonstrating how solutions enable the development of trustworthy, accurate, green and fair AI systems for food risk prevention
4 Groundbreaking advances in performance and usefulness of food risk data discovery, collection, mining, filtering, and processing
5 Integration of relevant technologies (such as big data, IoT, HPC, AI) to help achieve EFRA goals and foster links to respective communities of data innovators in the food supply chain
6 Positioning EFRA contributions into the overall ecosystem of public and private stakeholders that share data, technology and infrastructure to ensure the safety and quality of food in Europe
EFRA Project
Extreme Food Analytics Tools
DATA HUB
DATA HUB
A data platform able to search, mine, process, annotate, and link dispersed, heterogeneous, and deep/hidden food safety data sources.
Learn moreANALYTICS POWERHOUSE
ANALYTICS POWERHOUSE
An analytics and AI platform with an extensible set of modules running over a green cloud High Performance Computing (HPC) able to distil useful insights & signals from the EFRA Data Hub, and train green AI models for food risk predictions.
Learn moreDATA & ANALYTICS MARKETPLACE
DATA & ANALYTICS MARKETPLACE
A front-facing user-friendly web app that allows interested users to discover, purchase/use, and contribute with raw data, AI models, and analytics modules, thus creating an economy where data holders and data consumers can engage and trade.
Learn moreEFRA PROJECT
Real-world Use Cases
USE CASE
Risk predictions for poultry pathogens
EFRA will monitor and predict the presence of the most usual pathogens in poultry farms. Lab test and environmental data coming directly from farm sensors, will be combined with publicly available data sources, related to poultry industry (such as incidents for products and inspection results). Leveraging these, this use-case will deploy and train appropriate AI models for poultry pathogen predictions.
AI models for
pathogen predictions
AI models for
optimal pesticide use
USE CASE
Enhanced Predictive Capabilities for Pest Alarms
Although the farm pesticides must satisfy specific EU requirements, farmers need assistance for the optimal (instead of maximum-allowed) use of pesticides, when it comes to food safety. In this use case the focus will be in 3 EU countries: Poland, Romania and Croatia. By leveraging both aggregated data from at-the-farm sensors and publicly available data sources, we will deploy and train AI models, for optimal pesticide use recommendations.
USE CASE
Informing Regulatory Decisions with Food Risk Intelligence
To identify potential emerging food safety risks, EFRA will use extensive set of regulatory data and mining algorithms, while different individuals, companies and organisations (such as food companies and food safety authorities) will upload relevant data using the EFRA tools. By using a unique approach all these heterogenous data sources will be combined, enhancing the introduction of food safety regulations, with significantly low computational and energy waste.
heterogenous data sources
will be combined
Support effective strategies
for managing feed safety and
protecting animal health.
USE CASE
Mycotoxins in animal feed and effects on animal production
To analyze the presence of various mycotoxins in animal feed and to identify potential correlations among them. Additionally, it aims to explore the relationship between mycotoxin contamination and climate change. By understanding these correlations, the use case seeks to inform the development of more effective strategies for managing mycotoxin contamination, ultimately improving animal health and productivity.
EFRA PROJECT
Expected Results
Transforming
food risk predictions
with
AI-Powered Analytics
We believe that our works can contribute to a better world.
01
Innovative extreme data mining and analysis methods and tools
02
Data analytics and AI Prediction Models
03
EFRA Green Data and Analytics Infrastructure
04
Open Food Intelligence Network for public and private stakeholders
05
Customised business models for each use case
our team
EFRA Partners
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission-EU. Neither the European Union nor the granting authority can be held responsible for them.
2023 © EFRA project
