Computer vision and robotics for the agri-food industry

We combine non-destructive sensing, machine learning, and robotics to automatically and objectively measure products and processes. Our experts use 2D and 3D imaging, NIR, Raman and spectral imaging technology, and XRT to measure those key parameters.

Several of our solutions are commercial products where we take lab-scale results and ready them for real-world applications.Here we look at how all the information can be made applicable for actual practical use, ranging from classification of a batch of agri-food products to steering a sorting machine or robot. A close cooperation with product experts, software developers and industrial machine builders is crucial to ensure the correct translation into practice.

How we use machine (deep) learning
The data generated by the sensors is increasingly automatically analysed and interpreted via powerful machine learning methods. We have considerable experience in classical and modern learning methods. We frequently apply deep learning (Convolutional networks, Transformer networks, Large Language Models, Multi-Modal Models), as classical methods struggle to address the complexity of problems and data. These methods are used for identifying patterns in the data that correlate with the identified problems with higher reliability.