Monitoring sugar crystallization with deep neural networks

Abstract

Human labor still play an important role in cane sugar crystallization process. Automation control is essential to reduce human labor. An accurate image classification system is the basis for automation control of the cane sugar crystallization process. This paper builds a deep learning framework based on deep convolutional neural networks (DCNNs) to classify cane sugar crystallization image of cane sugar crystallization process for sugar factory. Different networks were trained on a large image data set obtained from a sugar batch crystallizer. Based on the data set, the established model was used to classify cane sugar crystallization image. The classification accuracy of the proposed model reached 0.901. The confusion matrix of the InceptionResNetV2 model indicates classification accuracy of between 0.83 and 0.99 are achieved in classifying cane sugar crystal images from a cane sugar factory into 5 categories. This provides a promising means for the future development of monitoring systems using image. The proposed DCNNs model was compared against other models, such as, Inception-V3, ResNet50, and a simple DCNNs. The experimental results showed that the deep learning framework outweighs other models and can serve as a benchmark of monitoring cane sugar crystallization using DCNNs in sugar industry.