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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 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 cheese wheels are automatically removed from the shelf by the care robot.
Interior view of the care robot, which can take care of up to three cheese wheels at the same time.
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.
Visualization of the inspected cheese wheels. On the left is how the wheel should look. “Prüfungen – NIO” (=inspections NOK) uses a heat map to show areas with defects. The image in the middle shows the result of the current inspection.
The cameras in the blue housing acquire images of the cheese wheels inside the care robot.
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."