Vision4Food

With the support of:

Vision4Food maps the advantages and limitations of machine vision in the food sector, specifically for your company, by developing practical knowledge and tools that enable data capture from various cameras on automation hardware. Through case studies, it supports food companies in selecting the optimal hardware for specific applications such as sorting, quality control, foreign‑object detection, packaging inspection, freshness analysis, and temperature measurements.

Why this project?

Implementing machine‑vision solutions in production processes for real‑time process control, quality inspection, and error detection is not straightforward. Challenges such as selecting appropriate technologies (both hardware and software) from the broad market offering, implementing these technologies within production processes, and integrating them into automation systems are addressed within Vision4Food. This lowers the threshold for implementing machine‑vision solutions by providing practical tools.

Research approach and expected results

The Industrial Vision Lab (UAntwerp InViLab research group) at the University of Antwerp, together with Flanders Make and Flanders’ FOOD, will provide practical knowledge and tools within the TETRA project Vision4Food to enable data capture from various cameras on automation hardware.

The project focuses on simple and flexible data acquisition, allowing different types of cameras to be easily read using user‑friendly, cost‑efficient, open‑source Python software. This software offers powerful AI libraries for image analysis and supports rapid technological development.

The integration of cameras into automation systems such as PLCs and robots is also simplified to enable real‑time process control and seamless hardware integration.

Furthermore, practical applications in the food industry are developed through case studies on sorting, quality control, foreign‑object detection, packaging inspection, freshness analysis, and temperature measurements during cooling and baking. These case studies investigate the added value and limitations of machine vision in the food sector.

Ultimately, Vision4Food supports companies in selecting the right hardware for specific applications, with attention to different types of cameras (visual, UV, thermal, multispectral, smart cameras, etc.) and the required lighting.

Target group

The Vision4Food project supports Flemish food companies in integrating machine vision into their production systems. The target group includes both large enterprises and SMEs.

For large companies, the focus is on support with technology and supplier selection. For SMEs, the project focuses on economically feasible solutions, such as open‑source tools, to make processes more accessible and affordable.

Project partners

Universiteit Antwerpen:

Faculty of Applied Engineering, Department of Electromechanics:
- InViLab: Prof. Steve Vanlanduit
- Co-Design for Cyber-Physical Systems Lab (Cosys-Lab): Prof. Amélie Chevalier
Contact UAntwerpen: Seppe Sels (seppe.sels@uantwerpen.be)

Flanders Make

Flanders’ FOOD will share the project results through study days, as well as through knowledge clips, case study reports, and demonstration videos that will be published on a project website and on YouTube. In this way, the information will remain available after the project has ended.

Keen to join?

As a member of the user group, you gain access to a wealth of knowledge, expertise, and networking opportunities that directly benefit your company. In addition, you have the opportunity to help steer the project during four project meetings.