Raspberry quality detection in visual spectrum using neural networks

The thesis presents the raspberry quality detection approach based on a convolutional neural network with U-net architecture and compared with PSPnet architecture. The limited possibility to use manual labour when growing, sorting, processing vegetables, fruits and berries in the face of increasing risks of new pandemics determines the study’s relevance. For the research, a neural network of the U-net architecture has been chosen based on the narrow focus of the task and small repetitive patterns. The neural network of the U-net architecture has proven itself well in solving problems of image segmentation in biomedical researches. Therefore, the author decided to expand the scope of this tool to a new area of investigation. The research is carried out on the data that the researchers have collected for the experiment. The dataset for the experiment has been generated manually based on images of different varieties of raspberries and various states of raspberry fruits. This research is expected to become a part of the complex robotic system for solving the problem of manual berry fruits sorting