All frozen and prepared food producers face a similar daily challenge: Ensuring that every tray of food produced looks like the food pictured on the packaging.

Many frozen food products are processed, assembled and packaged through automated machinery, often with a human inspector as the main line of defense to detect quality defects, unwanted indigenous material and potentially harmful foreign materials from ending up in the finished products. The physically demanding nature of these roles, combined with long hours in cold or industrial environments, can quickly contribute to burnout. Moreover, relying solely on human inspectors introduces subjectivity to the process, both from employee to employee and from the same employee over a single shift due to fatigue.

As consumer demand for frozen food products and prepared meals increases, greater product throughput requirements had led to a new era of automated vision inspection driven by artificial intelligence (AI).

Conventional Inspection in Modern Production Environments

For decades, most frozen food processing companies have relied primarily on metal detectors and X-ray systems for foreign material detection. While effective for detecting dense objects like metal or bone, these technologies cannot identify most low-density foreign materials like plastic, paper, rubber and like-colored objects that also pose a risk to the consumers.

Food production companies – primarily in baking and protein processing and several users within the frozen foods industry – began integrating vision inspection technologies within their processing lines. These systems incorporate high-resolution 2D and 3D cameras with advanced lighting to analyze all sides of the product, including top and bottom – for common quality measurements like overall color, size and shape, with detailed measurements like topping distribution, ratio of ingredients, presence/absence of components, predictive product weight, or the accuracy of imprints on a product (like a logo stamped onto a cookie).

Food processors need to know that vision inspection technologies are complementary to X-ray and metal detectors, not their direct replacement. Because each system is trained on a specific product, it provides an additional layer of detection by identifying anomalies that may go unnoticed by other methods. If the system identifies something outside its specifications, it can alert the operator to act or stop the production line.

Some companies have integrated automated rejection methods to remove out-of-spec products, to ensure that the defective products are removed from the process without causing line downtime.

Most vision inspection technologies used in food processing today are rule-based vision systems. In a rule-based vision system, as products pass under the vision system, the technology takes a picture of the product and compares the image against the user-defined measurement criteria. Companies used to measure only a few simple product attributes at a time, like product size, color or shape, which was much more achievable with a rule-based system.

New technology makes it possible inspect more complex product measurements simultaneously, including foreign materials, at full-line speeds. These increasing demands have pushed rule-based vision systems to their technological limits.

Rule-based systems will always be an option for food producers, but recent success by AI-driven vision inspection systems offer an exciting and powerful solution to replace rule-based vision and ensure product consistency and safety.

How Does an AI-Driven Inspection System Work?

AI-driven vision inspection technologies apply human-defined criteria to differentiate food product features and unwanted objects. An AI model can be developed to detect any difference a human can perceive.

Like a rule-based vision inspection system, an AI-driven inspection system incorporates high-resolution cameras and advanced lighting configurations to analyze products passing through the line. The AI system's ability to effectively segment individual food products and their ingredients from the overall image is what makes it far more effective than rule-based vision. An AI system goes beyond color detection or product shape and thickness to analyze complex systems for various attributes like how a human would.

For example, a frozen dinner manufacturer has several ingredients automatically dispensed into the product tray by multiple automated machines—noodles, protein, sauce, etc. While these ingredient application machines are inspected regularly they can malfunction or simply miss their mark when dispensing a product to the packaging.

A frozen dinner with its many different colors, shapes, and textures is challenging for a rule-based vision inspection system. However, with an AI-driven vision inspection system, it can immediately identify each individual component of the overall meal like a human inspector but with much greater precision and speed. The system can also instantly collect this data for real-time tracking or compile it into an easy-to-follow report.

Similarly, AI again applies human-grade precision to detect foreign materials and find objects within the product stream that do not belong. As an example, a manufacturer of individual quick frozen (IQF) diced onions may frequently run into the issue of hard onion skins entering the product stream which will lead to customer dissatisfaction for texture. Onion skin is typically close in color to the chopped onion, making it especially difficult for even the keenest inspector to differentiate.

The potential of AI to revolutionize quality control in frozen food production is undeniable. A study by Grand View Research estimates that AI in food and beverage markets will grow from $8.45 billion in 2023 at a CAGR of 39.1% from 2024 to 2030, with a revenue forecast of $84.75 billion. This growth opportunity reflects the increasing adoption of AI-driven solutions across the industry, which has naturally led to an increase in the number of new AI inspection system developers.

One key value of AI-driven vision systems is their objectivity. Unlike human inspectors, a well-trained AI system never takes a break, gets fatigued, or has an "off day" on the line. It applies measurements the human operator defines to analyze products for exact quality criteria. This way of AI training is called "supervised learning," which means human influence is always at the core of the system's function.

Through supervised learning, AI models are built based on showing the system both “good” examples and “bad” examples. Over time, the AI system learns the tolerances for a specific product feature or foreign material. Models can change or evolve as new product samples enter the training set, but the human operator or team of operators always remains in command.

"Unsupervised" AI training allows the AI to develop its own tolerances for products or materials. This method poses several risks regarding food production; because the AI is always seeking data, unsupervised AI systems may begin making incorrect decisions on product quality, prompting potentially disastrous results. It is crucial that an AI supplier can demonstrate or offer consistent training and support for the AI models over the system's life.

Smart Systems Ensure Safe Foods, Portion Precision

Adopting AI-driven vision inspection technologies offers frozen food manufacturers a significant competitive advantage in an industry with fierce competition and tight margins. These systems enhance safety and efficiency and demonstrate a commitment to innovation and quality, strengthening brand reputation and consumer loyalty.

By addressing emerging challenges and leveraging AI technologies, frozen food producers can enhance product safety, reduce operational costs, and meet the ever-evolving expectations of consumers and regulators. As AI advances, it will play an even more significant role in shaping the future of frozen food manufacturing, ensuring that quality and efficiency go hand in hand.

In the competitive and safety-critical world of frozen food production, embracing advanced AI technologies is not just an option but a necessity.