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AI-based sorting technology: TOMRA Food wins international FoodTec award for foreign material identification

The foreign material identification accuracy of TOMRA Food’s sorting and grading solutions has won a prestigious International FoodTec Award for “using artificial intelligence and cloud technology to improve food safety, quality, and traceability while increasing process efficiency.” 

Presented tri-annually by the DLG (German Agricultural Society), the International FoodTec Awards honor “ground-breaking developments in innovation, sustainability, and efficiency in the food technology sector.”

The panel of judges – from research, academia, and the food industry – awards gold medals for products with a new concept, and silver medals for “existing products which have been developed to such an extent that a substantial improvement in their function and their process is achieved.”

TOMRA received a silver medal for “Foreign Material Identification, powered by AI and TOMRA Insight.”  

Applying AI and deep learning

Artificial intelligence is hugely helpful for the potato market because of the highly variable nature of factors such as weather and incoming raw materials. When assisted by AI, sorting and grading machines can make more accurate ‘accept or reject’ decisions, recover more good product from compromised raw material, and more precisely classify the products on the line into different grades.

TOMRA’s solutions attain these advantages by adopting the AI method of deep learning. This uses pre-trained models to teach computers how to process data, such as complex patterns in photos. In combination with different sensor technologies these images of foreign material captured by TOMRA sorters are analyzed by a deep learning model. They help to constantly improve the solution by continuously retraining the model.   

Actionable data accessed via the cloud   

Felix Flemming, Head of Digital
TOMRA Sorting

TOMRA’s Foreign Material Identification solution is integrated with the cloud-based data platform TOMRA Insight. This automatically extracts data from sorting machines to provide real-time and retrospective insights into raw materials and machine performance.

Machine operators can respond instantly to TOMRA Insight’s live data via a user-friendly dashboard to make adjustments which improve product consistency. Stored data can be analyzed to obtain higher-level insights.

This enables trend-monitoring and comprehensive analyses across a single or multiple seasons, within a specific region, and across regions. These insights empower users to optimize processes, drive efficiencies, and enhance overall performance.  

Felix Flemming, Head of Digital at TOMRA Food and Recycling, commented: “We are pleased that the jury of the International FoodTec Award has recognized TOMRA’s industry-leading innovations in AI and cloud-technology, and the importance of these solutions to customers.

Marco Colombo, Global Category Director
Potatoes at TOMRA Food

“Digital solutions and AI are set to play an increasingly important role in meeting the needs and desires of food consumers, and in helping to feed the world’s growing population. This will require more food production and less food waste.”

Marco Colombo, Global Category Director Potatoes at TOMRA Food, said: “AI redefines the sorting game by improving precision and setting a new standard for crop sorting technology. New AI features are embedded in our sorting machines – as recently announced for our TOMRA 3A unwashed potato sorter – enhancing image processing by evaluating pixels in a more sophisticated way.

“While the objects are assessed down the line, we are able to evaluate how pixels are shaped against each other and we can classify clusters or an object as a whole.”

Source: TOMRA Food
Contacts:
Nuria MartíAlarcon & Harris PR, Spain
T: +34 91 415 30 20
E: nmarti@alarconyharris.com
W: www.alarconyharris.com
and
Michelle Haynes, VP, Head of Marketing & Communications TOMRA Food
Onehunga, New Zealand
+64 21 377 141
michelle.haynes@tomra.com

Editor & Publisher: Lukie Pieterse


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