ABP collaborates to drive leading research into the future of sustainable, healthy food

ABP Food Group has been taking part in a ground-breaking research project with assistance from the European Union which is aimed at redesigning the way food is produced, processed, consumed and recycled.

Building Europe’s future food system with the help of AI & data analytics

Beef and lamb producer, ABP, was selected to take part in the pan-European food partnership, EIT Food, back in 2017. EIT works as a consortium of over 50 partners from leading businesses, research centres and universities, including Queen’s University, to put Europe at the centre of a global revolution in food innovation and production, and its value to society. Its focuses to create a future-proofed and effective food sector that delivers solutions to transform the traditional “produce-use-dispose” supply chain model into a circular bio-economy.

As part of EIT, ABP is leading on a project that utilises the safe and effective use of 3D CT Scanning technology at its site in ABP Lurgan. The technology can accurately measure an animal’s composition and key features in 3D in order to advance improvements in the sustainable production, processing & consumption of beef & lamb.

ABP is supported by Siemens who are world experts in high technology industry, software & advanced 3D scanning innovations  along with Technische Universität München (TUM), a world leading technical institute in the field of Artificial Intelligence (AI) & Deep Learning for biomedical diagnosis & robotisation.

Sustainable, healthy food the consumer can trust

The three companies are working to expand & adapt hardware & software elements of 3D CT scanning technologies to digitise a key link in the beef and lamb food chain. Exact recording of meat composition, form and structure soon after slaughter can provide data that will help reduce waste, increase quality/value and improve traceability. The system can provide accurate feedback for farmers helping them to rear their cattle more sustainably whilst retailers and consumers can access information on food quality and taste preferences.

Business in the Community member ABP takes the lead in all aspects of the project including project management, feasibility assessement, cost benefit analysis, new butchery techniques, new meat process designs, hardware design & build, IP protection as well as software performance specifications.

Siemens is responsible for assessing the technological options to provide the best solution based on ABP’s requirements & input on system construction & implementation.

TUM and Siemens are also designing & implementing advanced AI based software solutions to automatically interogate the CT scans. These produce key composition outputs at high levels of accuracy and speed.

Data-driven decision making

Commenting on the project, Declan McDonnell, ABP Food Group’s R&D Manager said, “Meat tissue has a high degree of variablity in its composition. So the digitisation of beef and lamb in the supply chain will improve decision-making at all stages in the chain leading to improved quailty and consistency as well as a more environmentally sustainable product”.

Benefits of this world-first project include:

  • Informing breeding genetics to maximise, minimise waste & reduce feed usage
  • Informing husbandry practices to finish animals earlier with optimum feed regimes and avoidance of unecessary over-fattening
  • Enabling the processor to sort and match animal part composition to customer & consumer needs (eg fat content, portion size etc.)

“The digitisation of beef & lamb production is forming the foundation for a connected data flow in a consumer centric food system from farmer, processor to consumer and enabling the industry to produce product with optimum nutritional composition,” continued Declan.

Societal benefits

The project has the potential to introduce advanced engineering & AI software technologies outside of the meat industry. For example, the AI algorithms can be used in human biomedical research due to the commonality between animal & human anatomy, composition & morphology.