Spirits quality classification based on machine vision technology and expert knowledge

By combining machine vision technology and expert knowledge, this paper proposes an online intelligent classification solution for Chinese spirits, which effectively improves the classification accuracy and production efficiency of spirits. Specifically, an intelligent spirits quality classification system is first designed, including spirits collectors, image sampling cameras, and computing devices. According to the principle that the size and shape of the bubbles in the spirits collector will change with the alcohol content in the spirits, a classification method of spirits quality based on the convolutional neural network (CNN) and bubble region of interest (ROI) selection is proposed. Furthermore, a post-processing method based on expert knowledge is proposed to improve the accuracy of the classification algorithm. A spirits quality classification dataset containing 139 119 images is created, and 15 CNNs are tested. Test results show that the highest spirits quality classification accuracy is 98.62% after using the bubble ROI selection method, and the highest classification accuracy reached 99.82% after adopting the post-processing method. Furthermore, practical application tests show that the solution proposed in this paper can improve spirits' production quality and efficiency.