AI AND ROBOTICS Advancing food safety with AI


A report from BCC Research indicates that AI technology applied to areas such as contaminant detection, traceability and compliance is projected to grow at a CAGR of 30.9% by 2030. This is expected to revolutionise food safety and strengthen quality control.


“Machine vision is integral to this key trend,” argues Phil


Brown, Sales Director at Fortress Technology Europe. “Forming part of a larger inspection system, adding vision capabilities to existing technology – for example metal detection or X-ray – strengthens quality control by capturing an image and processing it against set quality control parameters.” One of the most valuable ways to contribute to a safer, more


secure and sustainable food supply chain, as well as maintain a competitive edge, is through process and operational optimisation. “Leveraging smarter technology and predictive analytic tools can help to create a safer, digitised and more traceable food system,” continues Phil. Fortress, for example, is now using a proprietary data


software package across its metal detection and checkweighing technologies. This enables processors to review, collect data and securely oversee the performance of multiple metal detectors, checkweighers or combination inspection machines that are connected on the same network. “Deployment of this type of data reporting provides context to support rule-based machine learning. It also enhances human decision-making through the extraction and interpretation of data,” says Phil. Intuitive data management and the accessibility of AI now


make it possible to integrate any combination of inspection technologies – metal detection, checkweighing, X-ray and vision. When integrated into a single system, this synergistically enhances the performance of each technology. “The addition of vision and AI to inspection technologies is


especially exciting as it allows for the collection of comprehensive data on each inspected pack. In the future this could include details on weight, size, visual integrity, contaminant detection results and adherence to quality standards,” concludes Phil.


in robotic pick and place applications. He said: “When the packaging line starts up, the image processing system loads up the necessary information and search specifically for articles that meet these criteria. Products that fall through the grid are not even registered by the devices and so are not picked by the robots. These data packets can be reset at any time via the machine’s user interface by the operator.” Gently does it


Most confectionery products require gentle handling which demands the right robot tools along with precise robot movements. “Depending on the structure of the product, suction or gripper tools can help to ensure gentle handling,” continues Daniel. “Suction tools are suitable for smooth, dense, non-slip surfaces, and for flat, relatively rigid products or packaging. Grippers are required for fragile, soft or non-vacuumable products, or those with a porous surface.” However, no tool or robot arm can


achieve sufficiently gentle handling without a precisely calculated trajectory. AI also has an important role to play here, where it is providing support in the form of higher robot speeds and smoother movements, resulting in shorter cycle times. “This is made possible by new algorithms that enable accelerated calculation of motion profiles, ensuring safe product handling even during fast processes,” concludes Daniel. In inspection and quality control


applications, AI and machine vision systems are helping to ensure the detection of defects in confectionery products. “These systems can analyse products for inconsistencies in size, shape, or colour to maintain high quality in an


with high precision,” he said. Manual sorting is inherently prone to errors with factors like fatigue and repetitive tasks contributing to this. Some defects are also simply too difficult to identify through basic visual inspection alone. Robots, equipped with AI and vision systems, however, are able to inspect confectionery items for variations in colour, shape, or size, ensuring that only products meeting high quality standards make it through to the packaging hall. In packaging applications, AI enabled


efficient way,” says Edouard Perona, Global Market Leader Food Robotics Division at STÄUBLI TEC-SYSTEMS. “The precision of machine vision improves the efficiency of packaging processes too. Robots, guided by AI and machine vision, are more flexible and can sort and pack confectionery items with minimal waste and optimal speed into a variety of different packages.” Additionally, AI-driven data analytics


optimise supply chain operations, with performance data feedback being used in the production process. Having data relating to where, when, and why deviations and defects occured makes it possible to improve the whole process – across planning, maintenance, and resource allocation. “This shift from reactive to proactive quality control marks a new transformation in how factories can approach their production strategy,” explained Edouard. “Quality control solutions have


benefitted from the use of robotic vision and AI systems that are able to inspect product consistency and detect defects


48 • KENNEDY’S CONFECTION • DECEMBER/JANUARY 2025/26


robotics improves efficiency and speed. “Automated systems can quickly sort and pack confectionery products, adapting to different packaging designs without the need for extensive adjustments,” continues Edouard. “This flexibility is crucial in handling diverse and custom packaging demands, optimising the production flow. Product handling is another area


where robotics can have an impact on the confectionery production process. “Robots can reduce the need for manual labour across many stages of production. They ensure good coordination between different processes, reducing downtime and increasing overall operational efficiency.”


Step changes The integration of AI into robotic systems looks set to redefine what is possible in confectionery production. By extending the core capabilities or robotic technology AI is enabling robots to operate with a level of flexibility and precision that was not previously possible and confectionery manufacturers now have an opportunity to build smarter, more resilient production systems that can respond dynamically to operational and market demands.


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