Abstract. This study focuses on the segmentation part in the development of a potato-sorting system that utilizes camera input for the segmentation and classification of potatoes. The key challenge addressed is the need for efficient segmentation to allow the sorter to handle a higher volume of potatoes simultaneously.

To achieve this, the study employs a region-based convolutional neural network (R-CNN) approach for the segmentation task, aiming for more precise segmentation than classic CNN-based object detectors. Specifically, Mask R-CNN is implemented and evaluated with different parameters to achieve the best performance. The implementation and methodologies are thoroughly detailed, and the findings reveal that Mask R-CNN models can be used in the production process of potato sorting to improve the process.