Food & Beverage
AI in the food industry
In line with the increase of the world population and the rapid expansion in the hotels, restaurants, and catering sectors and the recent establishment of fast service restaurants, a shift for AI in the industry has become a necessary thing. Industry pundits have predicted that AI in Food & Beverages Market size is expected to grow from USD 7.00 billion in 2023 to USD 35.42 billion by 2028. They saw that AI in the food industry will increase production, improve supply chains and reduce human errors. For example, it can help reduce contamination in food production which can lead to a safe product, improves all operational practices including food transportation and service quality.
product, improves all operational practices including food transportation and service quality.product, improves all operational practices including food transportation and service quality.
product, improves all operational practices including food transportation and service
How AI is Applied in Food Industry
Ai can be applied in different ways depending on the day-to-day tasks in the industry This can vary from the segregation of food ingredients to its packaging for delivery to the final customer. AI can utilise vision recognition, a feature of machine learning to sort items at the final step of the automated process, removing items that don’t fit to the quality standard. This feature is doing its job according the quality images it was fed. Base on the images, the system is comparing the feed datasets to the product and apply the rules.
While many people associate vision recognition with Facebook because of its and its capability to “recognize” people faces in different images and tag them, it was found that it also has some real potential value in the manufacturing process. The technology built in security devices to identify and recognise unidentified individuals who are trying to enter your building can also recognize if a potato chip does have the perfect golden colour or not.
Error Detection. Banks have been using AI for years to determine when “atypical” charges are being made against your account, triggering a fraud alert. The same type of monitoring for anomalies can be used by manufacturers to flag orders or purchases which are outside of normal patterns and indicate a possible error. It can also be used to monitor for compliance with regulations, workforce safety and the safe handling of food, including the cross contamination of typical allergens, like peanuts. The ability to “intelligently monitor” for potential liabilities means modern software can act as a watchdog and alert any abnormalities that we humans should investigate.
Gutentor Simple TextSorting and Quality Control
Tomra Systems ASA
Tomra Systems ASA offers a line of food sorting machinery with analytics capabilities which it claims can help food manufacturing companies perform automated food analysis, such as measuring the size, shape, and color of french fries or analyzing the fat content in meat using shape recognition technology, a subset of machine vision.
Tomra claims that clients can integrate the POM/DYN french fries length analyzer with their own existing french fries production machinery.
We can infer that the machine learning model behind the software was trained on images of french fries of different sizes and shapes from various angles and in various lighting conditions. These images would have been labeled as french fries of the correct length, for instance between 3 to 4 inches, and those that are too short or too long.
These labeled images would then be run through the software’s machine learning algorithm. This would have trained the algorithm to discern the sequences and patterns of 1’s and 0’s that, to the human eye, form the image of a french fry of the correct length and those that are too short or too long, or one that is discolored and possibly contaminated, as displayed on in an image on a dashboard.
The machine operator could then run the POM/DYN camera over the actual french fries, and the algorithm behind the software would then be able to discern the pieces that are between 3 to 4 inches, which are accepted on the main stream of the machinery. The pieces that do not meet the specified length are redirected to another stream. Through the LDC display, the system shows the machine operator a single random sample of a french fry that passed the shape, color, and length standards and compares it with a sample of a french fry that is rejected.
Below is a short 2-minute video demonstrating how Tomra’s sorting equipment is able to discern the correct shape, length and color of a french fry and separate fries that do not meet the specifications:
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