Ensuring Label Accuracy in the Food Industry – A Real Case of Zero Tolerance for Error

In the food sector, precision is not a luxury — it’s a requirement. Especially when it comes to labeling. One incorrect label can lead to serious consequences: product recalls, legal penalties, and above all, a loss of consumer trust. That’s why a leading Spanish manufacturer of packaged meat products decided to act when their existing vision system failed to detect a key labeling error.

The Problem: A Missed Error That Couldn’t Be Ignored

The company — a well-known producer of sliced meats and ready-to-eat trays — had already invested in a camera-based vision system to monitor product labeling. However, despite the hardware in place, an error in a production batch went unnoticed: the label was applied, but its content did not match the product inside. This mismatch represented a major risk, especially considering allergen and traceability regulations.

This incident was a wake-up call. The existing system was unable to verify the semantic and visual correctness of the labels — it only checked their presence. The customer needed a smarter solution.

Our Response: A Fully Trained AI Model Using Real Client Data

Rosepetal AI responded with a highly specialized solution: a custom-trained deep learning model, built using the customer’s own images and production data.

This wasn’t just about adding software — it was about teaching the system to “understand” the label: its layout, its expected text, and its acceptable variations.

Key technical highlights of our solution:

• OCR (Optical Character Recognition) to read dates, codes, and batch numbers directly from the label

• Position verification to ensure the label is correctly placed on the tray

• Text content recognition, allowing the system to validate the printed product name against the packaging

• Integration with the existing industrial camera, with no hardware replacement needed

• Real-time communication with the production line, allowing for instant rejection of incorrect units

Our solution adapted to multiple product formats across different lines — all within a demanding industrial environment where speed and humidity are daily challenges.

Results: Zero Labeling Errors, Full Confidence

Just weeks after deployment, the results were clear:

• 0 % labeling errors detected since the system went live

• Automatic rejection of any mislabeled unit — in real time

• Full traceability thanks to automated reports for quality audits

• No need to modify existing camera setups

• Increased speed and reliability of quality control without human intervention

Why It Matters

This case shows that hardware is not enough. You can have cameras, but without intelligent interpretation, critical errors still go unnoticed. Rosepetal AI’s software adds the missing intelligence to existing systems — unlocking real, measurable quality assurance in sectors where compliance is everything.

If your production line handles food products, are you truly verifying what your labels say?