As part of the Vencomatic Group, Prinzen is a solution provider for egg handling and developed the Meggsius Select for automatic egg inspection and sorting. This system inspects every individual egg on exterior indicators such as quality and dimensions, and can detect egg-shell fouling and cracks. For this, we developed a machine vision system and the accompanying inspection software based on AI (artificial intelligence), in this case deep learning using neural networks. It enables the sorting of first- and second-grade eggs and, in addition, yields data on egg quality and reject categories (per hen house and/or day totals) that can be used for further analysis.

For imaging the individual eggs, we designed a smart lighting scheme. During the process, the eggs are repeatedly rotated over 90°, to collect images of all views. The combined set of images are used for egg inspection using various deep learning networks for feature extraction: egg localization, egg contour detection and classification of defects such as cracks and fouling. The system can process 40,000 eggs per hour, which requires a lot of computing power. To optimize the trade-off between processor power and algorithm quality, we trained and tested multiple AI networks in a genetic algorithm to arrive at an optimal, efficient network.