From breaded chicken patties to chocolate chip cookies, the production technologies and throughput speeds of today's food processing facilities have changed dramatically in recent years. This transition towards greater automation and less manual involvement is evident from the time raw ingredients enter the facility until the final product is packaged.
Despite these emerging production methods, many food processors still heavily rely on manual product inspectors as the final line of defense to validate final product quality and detect harmful foreign materials that can lead to costly product recalls. Common food safety technologies such as X-rays and metal detectors, which are good at detecting metal or bones, struggle with low-density or non-metallic foreign materials such as plastic, rubber, wood and objects similar in color to the food product.
To address these limitations, the food processing industry is increasingly turning to artificial intelligence (AI)-driven vision inspection systems. These systems offer enhanced detection capabilities and operational efficiencies that meet the challenges of modern production environments.
A new era in food product inspection
In-line vision inspection technologies are not new to the food industry. Some rule-based vision inspection systems have been used successfully in production lines for decades. These systems combine high-resolution 2D and 3D cameras to analyze all sides of a product for pre-programmed visual traits, including the bottom of products. From the overall size, shape, color and marbling of a steak cut to the seed distribution on a sesame seed hamburger bun, rule-based vision systems effectively detect finite product details that are nearly impossible when operating at full-line speeds.
In-line vision systems can also be integrated with rejection mechanisms to automatically remove out-of-spec products from the process stream or alert the operator to act.
As the demand for these systems has increased, rule-based vision systems are being stretched to their technological limits. Producers used to be satisfied with inspecting a handful of product aspects; now, they are interested in much more. For instance, going back to the hamburger bun example, many quick-serve restaurant (QSR) chains now prefer a bun with a consistently shiny product surface. However, this shine impacts the color measurement from the vision system. Companies can capture this traditionally difficult-to-measure product feature with the correct lighting schematic, software and camera setup.
These increasing demands have led technology providers to explore AI to streamline measurement model building for product inspection systems. Automated machine learning of product specifications and features dramatically reduces system complexity and streamlines product setup, helping companies achieve greater heights in quality and food safety control.
AI inspection goes beyond colorimeters, X-rays and metal detection systems by applying deeper analysis of the product surface. AI is trained to detect what a human can see and beyond.
AI enables complex measurement capabilities
As an example, let's use a frozen pizza manufacturer who is challenged to produce a consistent product. Most pizza plants have multiple stages where ingredients are automatically applied to the product as it passes through a conveyor. This fast and, at times, chaotic process is often challenging for a human operator to control accurately.
Each pizza has multiple toppings, textures and colors. With rule-based vision, the operator would have to program individual measurements for each ingredient on a pizza and layer them into the overall product measurement. Depending on the type of pizza, these measurements can require immense computing power and measurement convolution.
Now, with AI inspection, the pizza operator can train the software to immediately inspect the entire pizza and all its complexities with elite precision.
Similarly, the AI-driven inspection system also knows when products are not at ideal specifications. For example, some frozen dinner manufacturers use AI systems to ensure food ingredients are administered directly into the correct packaging components, determine if any products are missing (e.g., meatballs in a Swedish meatball frozen meal) or validate product packaging.
A building case for AI in food safety
Food processing companies have long faced challenges in retaining and training on-site product inspectors, even though these roles are critical to ensuring quality and food safety. However, the stakes are high — just one product recall due to undetected harmful foreign materials can cause severe financial losses and tarnish a brand's reputation in the marketplace.
The financial impact of a product recall in the food industry can be staggering. According to research by the Food Marketing Institute and the Consumer Brands Association, the average recall cost ranges from $10 million to $30 million. These costs include notifying consumers and retailers, removing products from shelves, disposing of contaminated items and managing the resulting public relations challenges.
Food processing companies are increasingly turning to AI-powered inspection systems to mitigate these risks. By investing in advanced systems to detect potential hazards, companies can safeguard their operations, protect their customers and reduce the likelihood of costly recalls.
Additionally, for meat, poultry and seafood processing plants that have strict sanitary environments, AI inspection systems exist to meet washdown requirements (IP69K, NEMA-4X, etc.). For instance, companies that process beef trim use these sanitary inspection systems to identify undetectable foreign materials by X-ray and metal detectors. Because beef trim is rarely uniform or consistent, rule-based vision systems have difficulty discerning good products from harmful materials. However, an AI system can instantly compartmentalize everything it analyzes on a processing line and instantly identify materials it deems outside the acceptance criteria.
Detecting foreign materials before they can reach later processing stages can save companies many millions of dollars over the system's service life. This not only delivers a speedy return on investment but also helps safeguard consumer confidence in a brand.
AI provides support where plants typically struggle
AI will continue to be a hotly debated topic worldwide, especially regarding its impacts on replacing human labor in various industries and responsibilities. However, labor retention in crucial quality assurance roles has always been difficult in the food industry.
AI inspection technologies effectively address these needs by applying 100% objectivity to their inspection process — something that cannot be achieved with a human operator. Rather than having to spend significant time, money and effort training employees on these important yet mundane and repetitive tasks, the company can give those people more meaningful responsibilities elsewhere in the plant.
Additionally, a fundamental principle in deploying AI vision inspection systems is the necessity of human involvement in training and maintaining these systems. AI applications achieve optimal success when guided by human expertise, mainly through supervised learning methodologies. Therefore, AI will never completely replace the human factor in food production; instead, it will equip plants to effectively meet rising demands without sacrificing quality or food safety efforts. As AI technologies evolve quickly every day, it will be used more widely in food processing industry to ensure safer and better-quality food at higher throughput and lower cost.
