The food industry is experiencing a transformative shift in quality control, due in part to advances in artificial intelligence (AI). When combined with rule-based machine vision, AI is enabling automation of processes that were previously impossible, unlocking new levels of productivity and quality assurance. One such breakthrough has been developed by Eberle Automatische Systeme, a leader in automation solutions, with a focus on the cheese-ripening process.
The Challenge: Rising Demand, Labor Shortages, and Sustainability:
Cheese consumption is booming globally, and producers are facing increasing challenges as they scale production. Labor shortages, particularly in Europe, are pushing dairies to adopt automation to increase efficiency. Meanwhile, sustainability is becoming a central concern, with an increased focus on reducing waste and conserving resources. Additionally, consumers are demanding higher-quality products with more variety, further intensifying pressure on producers.
As Eberle’s Machine Vision Engineer, Dorian Köpfle, explains: “The cheese-ripening process, which can last up to 14 months, requires constant monitoring to avoid mold and ensure quality. Manually inspecting thousands of cheese wheels is virtually impossible, which is why Gebr. Baldauf GmbH & Co. KG, a traditional dairy, turned to us for an automated solution.”
The Solution: Automation with Machine Vision and AI
Gebr. Baldauf, located in the Allgäu region, commissioned Eberle to solve these challenges. The result is a fully automated monitoring system, that combines a mobile care robot, cameras, and onboard image processing. The process begins with the inspection of cheese wheels for defects, such as mold spots or blemishes. A 4K camera captures high-resolution images, which are analyzed using advanced machine-vision algorithms from MVTec HALCON. The software uses deep-learning methods to detect anomalies earlier, minimizing process deviations and waste. The data is stored and made available via a web interface, enabling remote monitoring and control. Simultaneously, the mobile care robot performs its task of treating the cheese wheels, ensuring proper rind formation and removal of unwanted smear layers. This system not only increases efficiency by reducing manual inspection but also improves the consistency and quality of the final product.
Key Outcomes and Business Impact:
The deployment of this automated system has provided several key benefits for Gebr. Baldauf, including: Increased Efficiency: The mobile care robot operates autonomously, reducing manual labor while ensuring that each cheese wheel is inspected and treated thoroughly.
Waste Reduction: Early detection of mold or defects allows for timely intervention, preventing rejected cheese and minimizing waste.
Improved Quality Control: The system ensures more consistent and less subjective inspection results by replacing manual methods with AI. As a result, the process achieves a 100% inspection rate, applying the same inspection criteria throughout.
Full Traceability: The integration of industrial image processing ensures complete product traceability. All inspection results are stored digitally for easy access, enabling better decision-making and long-term process optimization.
Overcoming Technical Challenges with AI:
A significant challenge in developing this system was the natural variability of cheese. Every wheel looks different and undergoes significant changes during the ripening process, which makes rule-based machine vision methods less effective. To overcome this, Eberle utilized AI and deep learning to create a system that could adapt to the unique characteristics of each cheese wheel.
The MVTec HALCON software was instrumental in this process. By training a deep-learning network with a large dataset of cheese images, the system is able to reliably detect defects such as cracks, mold, and discoloration, while ignoring the natural variations inherent to the process. This technology ensures that even subtle anomalies are spotted, allowing for earlier intervention and better quality control.
Enabling Full Automation
The Path Forward Eberle’s goal was not only to automate the inspection process, but to fully integrate AI into the cheese-ripening workflow. Currently, the system is capable of performing real-time inspections and autonomous care, with minimal human involvement. However, the company is working on refining the system further to handle all types of cheese and stages of ripening, with the long-term goal of creating a fully automated, AI-driven system that requires no human input.
The system also provides a solid foundation for future digitalization efforts, with the potential for integration into larger digital platforms, such as ERP systems and the cloud, to further optimize the production process.
Looking Ahead: Scaling and Further Digitalization
Building on the success of this project, Eberle is now focused on scaling the solution to meet the needs of the entire cheese industry. The company plans to standardize the system and integrate it into both mobile and stationary care robots for cheese production worldwide.
Furthermore, the system’s AI capabilities are continually evolving. Eberle aims to refine the deep-learning models to handle different cheese types and ripening stages, enabling fully automated classification and inspection. This will allow producers to further reduce human involvement while maintaining the highest standards of quality.
As Christoph Muxel of Eberle summarizes, “Our machine vision-based solution demonstrates how automation can sustainably improve quality, efficiency, and competitiveness in the food industry. This project is just the beginning, and we’re excited to take these innovations to a global scale.”
About MVTec Software
GmbH MVTec is a leading manufacturer of standard software for machine vision. MVTec products are used in a wide range of industries, such as semiconductor and electronics manufacturing, battery production, agriculture and food, as well as logistics. They enable applications like surface inspection, optical quality control, robot guidance, identification, measurement, classification, and more. By providing modern technologies such as 3D vision, deep learning, and embedded vision, software by MVTec also enables new automation solutions for the Industrial Internet of Things aka Industry 4.0. With locations in Germany, the USA, France, Benelux, Spain, China, Taiwan and South Korea as well as an established network of international distributors, MVTec is represented in more than 35 countries worldwide.
MTN Analysis
This project matters because it shows a realistic, commercially relevant use of AI in food manufacturing. Rather than presenting AI as a future concept, Eberle and Gebr. Baldauf are applying it to a specific production bottleneck where manual inspection is difficult, labor-intensive, and inconsistent.
The combination of robotics, machine vision, and deep learning is especially significant because cheese is a naturally variable product. That makes it a strong test case for industrial AI. If AI can reliably distinguish between natural variation and real defects in a changing biological product, it can provide valuable lessons for many other food and beverage applications.
For manufacturers, the wider message is clear. AI inspection is becoming less about experimental pilots and more about measurable operational gains, including waste reduction, labor efficiency, traceability, and better process control. In sectors where margins, quality standards, and labor availability are all under pressure, those gains can quickly become strategic.
Machine vision inspection systems powered by deep learning are already transforming industrial automation, as explored in our article on AI machine vision systems using HALCON and MERLIC.
FAQ
What is AI in cheese production?
AI in cheese production refers to the use of artificial intelligence, often combined with machine vision and automation, to inspect, classify, monitor, and optimize cheese-making and ripening processes.
Why is machine vision important in cheese ripening?
Machine vision allows producers to inspect cheese wheels continuously and consistently for defects such as mold, cracks, blemishes, and discoloration. This is difficult to achieve manually at scale.
Why did Eberle use deep learning in this project?
Deep learning was needed because cheese varies naturally in appearance and changes throughout the ripening process. AI helps the system distinguish between normal variation and actual defects more reliably than rule-based inspection alone.
What are the main benefits of this automated system?
The main benefits include higher efficiency, reduced manual labor, lower waste, improved inspection consistency, 100% inspection coverage, and full digital traceability.
Which software was used in the solution?
The inspection system uses MVTec HALCON machine vision software, including deep-learning methods for defect detection.
