Food Image Analysis

Some time ago we have demonstrated the utility of magnetic resonance imaging in food quality assessment. When the cost of this imaging modality decreases, we will be ready to apply our solutions for assessing the quality of food. By analyzing the texture of the image in cheeses, we evaluate the distribution of internal holes. As strange as it sounds, the type and arrangement of the holes in the cheese makes a difference. A similar analysis of color characteristics and texture of cold meats enables evaluation of grinding and composition, including fat content. After all, we want to eat tasty but not greasy. The results of our work also show that on the basis of tomographic images we can recognize varieties and we can qualitatively characterize vegetables and fruits, including potatoes, carrots, apples and probably many others.

We also have interesting information for those over the age of 18 and 21, for those who can and do appreciate the taste of beer. We can recognise varieties of malting barley from photographs. We have also shown that analysis of the shape, texture and colour of the surface of barley grains makes it possible to assess their quality, recognise typical damage, infections, and sort them.  

In image analysis, we not only apply available solutions such as deep neural networks. We mainly use our own developments, algorithms and software for research on food shape, colour and texture. We make some of our software available to others. An example is the open-source qmazda project, which includes tools for image segmentation, data extraction and classification. Using our programmes, it is possible to analyse plant varieties, assess food quality and build solutions to support producers, processors and consumers. We encourage all interested parties to contact us and collaborate.