AI in Food Inspection: Predicting Defects Before They Happen
Imagine a production line where machines don’t just detect defects but learn to predict them before they happen. A world where every biscuit, cracker, and chocolate bar is scrutinized with superhuman precision—ensuring that only the best make it to the consumer. This isn’t science fiction – it’s the reality of artificial intelligence (AI) in inspection technology.
AI-driven quality control is going far beyond conventional inspection systems. No longer limited to simple data collection, AI now predicts trends, spots inefficiencies, and makes real-time decisions that optimize production and reduce waste.
But how exactly does it work, and what does it mean for the future of food manufacturing?
Modern machines are equipped with smart cameras and AI-based tools that observe, learn and adapt. From the connection of the machine, to the precise recording and administration of operating and machine data, through to the dynamic visualization and analysis thereof – the systems enable total transparency of the production processes in real-time.
Process Transparency in Real-time
“The potential of AI and machine learning is huge and will fundamentally change the processes of the companies – also in the field of quality assurance,” emphasised Guido Hentschke, Director of ProSweets Cologne, with regards to this year’s highlights of the leading business platform for the global suppliers of the sweets and snacks industry. Instead of “just” recording data, AI can analyze trends and predict future results. By using advanced algorithms it reveals hidden inefficiencies and delivers recommendations of action to increase “the reliability and flexibility of the production and optimize the use of resources,” Hentschke stated.
Automated inspection systems are one of the most important AI applications in the sweets and snacks industry. Thanks to the implementation of computer vision and algorithms of machine learning, modern solutions offer an unprecedented level of precision – for example in recognizing defects in biscuits, wafers and crackers. Whether round or square, sweet or savory, made of wheat or oats: Even slight deviations on complex surfaces are detected on the conveyor belt directly after leaving the continuous oven – this minimizes production stoppages and waste and goes hand in hand with the producers’ commitment towards more sustainability.
Visual Quality Control Intelligently Optimized
The special feature is that AI assesses the products individually and allocates quality indicators. Holes, breakages, insufficient coating and oozing chocolate are labelled as rejects. Deficits like bubble entrapments or smaller scratches are also detected, but here there are higher tolerances. The quality controls not only have to recognize cracks or color defects. Foreign bodies have to be detected immediately, before the bakery products reach the trays.
Users can thus carry out complex sorting and quality controls for irregularly formed items, which is difficult, if at all possible, to carry out using rule-based vision systems. In contrast to humans, AI systems are able to scan hundreds of products a minute continually and find tiny flaws or contaminations, which could impair the quality of the food. AI especially demonstrates its advantages in highly-automated packaging lines where the priority lies on speed, flexibility and efficiency. This ensures that only goods that meet the strict quality demands reach the consumers.
An Eye on Everything During the Snack Check
In addition to the established R(ed)-G(reen)-B(lue) camera technology and the laser scan, more and more systems that work in the ultraviolet or infrared wavelength range have recently been implemented to inspect food. The reasons for this are tasks that can no longer be solely solved using sensors that work in the visible wavelength range. Here, the hyperspectral image processing of the Austrian company, Insort, for example, reaches down to molecule level. It allows the chemical composition of the products to be assessed spatially-resolved inline and in real-time. And even if test objects with a higher variance have to be inspected and sorted, like dried fruits and nuts, AI is no longer a future vision. With the aid of Deep Learning, modern vision systems like the Sherlock Hypernova by Insort decide themselves whether an object belongs in a snack mix or whether it is a foreign body. All foreign bodies, whether plastic, stones, metal or fragments of glass are removed in just one step. It is also possible to determine the bitterness of almonds and have them discharged safely, where necessary.