In the world of industrial bakeries, consistency is everything. A leading European bakery recently faced a quality challenge with one of its most iconic products: the baguette.
While demand remained strong, the production line suffered from irregularities; variations in length, height, shape, and even surface cracks, which not only affected product appearance but also led to rejections by large retail chains.
The Challenge: Non-uniform Baguettes at High Speeds
“Operating at a pace of 120 units per minute, the bakery needed a way to inspect every baguette in real time without interrupting production. Traditional quality control methods based on weight or manual checks could no longer keep up with the required precision”, mentioned Saad Aghchmi in a recent interview with Bakery Industry Insider.
The company sought an automated system that could:
Detect deformities (twisted, pinched, cracked loaves)
Measure height and length with high accuracy (±10 mm height, ±20 mm length)
Classify unacceptable products and trigger rejection
Be flexible enough to adapt to various baguette formats Integrate seamlessly with the existing conveyor and PLC systems
The Solution: Vision-Based Inspection Powered by Deep Learning
The answer came in the form of a custom deep learning solution using artificial vision technology. A robust stainless-steel system was installed directly on the production line, equipped with:
A high-resolution color matrix camera
A linear laser scanner for precise height measurement
Controlled lighting inside a dome to avoid external disturbances An intuitive 15” touchscreen interface for operators
A deep learning-based software platform trained with real images
According to Saad, “Operators used a cloud-based training tool to feed thousands of labeled images into the system, allowing the neural network to learn the difference between acceptable and defective products. Over time, the AI model became increasingly accurate, even recognizing subtle surface inconsistencies invisible to the human eye”.
The Result: Real-time Quality Assurance and Reduced Waste
The new system now detects cracked or deformed baguettes in milliseconds, sends signals to the rejection system, and compiles error statistics for continuous improvement.
It not only improved visual quality but also reduced product waste, minimized customer complaints, and increased line efficiency.
“Thanks to the flexibility of deep learning, the bakery team can now train the system themselves to recognize new product variations without external support. Quality assurance is no longer a bottleneck; it’s a competitive advantage.”
Takeaway: Deep Learning Isn't Just Hype—It’s Working in Real Bakeries
This case shows how AI-driven inspection can address real production problems in the baking industry. When product aesthetics are as important as taste, having a scalable and accurate quality control system makes all the difference.
👉 Thanks for reading!
AIS Vision Systems in Collaboration with Bakery Industry Insider
Contact Information:
Website: www.aisvision.com
Phone: +34 931 003 035
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