{"id":7241,"date":"2024-03-06T19:07:15","date_gmt":"2024-03-06T17:07:15","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&p=7241"},"modified":"2025-08-15T09:35:52","modified_gmt":"2025-08-15T06:35:52","slug":"2024-validation-of-a-multi-purpose-quality-control-system-operated-by-artificial-intelligence-for-food-industry-production-lines-at-noo-lihatoostus","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2024-validation-of-a-multi-purpose-quality-control-system-operated-by-artificial-intelligence-for-food-industry-production-lines-at-noo-lihatoostus\/","title":{"rendered":"2024: Validation of a multi-purpose quality control system operated by artificial intelligence for food industry production lines at N\u00f5o Lihat\u00f6\u00f6stus"},"content":{"rendered":"

The demonstration project addresses the challenge of performing multiple parallel quality control tasks simultaneously on a fast production line. Currently, these tasks are carried out by workers, but at a speed of 80 packages per minute, many errors go unnoticed. The project’s goal is to validate an AI-based system capable of checking multiple quality issues simultaneously, such as product and package compliance, labeling, and expiration date. The AI technology employed uses convolutional neural networks along with few-shot learning methods, enabling the system to quickly recognize new products without engineering intervention. The expected outcome is to reduce defective production by 10 tons annually and decrease labor costs on production lines by around 10%. Additionally, the solution supports the implementation of quality standards in the company, helping save costs and reduce waste.<\/p>\n

 <\/p>\n

Github<\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

The demonstration project addresses the challenge of performing multiple parallel quality control tasks simultaneously on a fast production line. 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