Abstract
Automatic tea-category identification is an important topic in factories and supermarkets. Traditional methods need to extract features from tea images manually, which may not be optimal for tea images classification. To avoid the time consuming efforts of handcrafted features extraction, this study proposed a new method combining convolutional neural network (CNN) with stochastic pooling. We collected 900 tea images of Oolong, green, and black teas, with 300 images for each category. The data augmentation method was used over the training set. We employed stochastic gradient descent with momentum (SGDM) to train the CNN. The experiments showed that a 12-layer CNN gives a good result. The sensitivities of Oolong, green, and black tea are 99.5%, 97.5%, and 98.0%, respectively. The overall accuracy of all three-tea categories is 98.33%. The stochastic pooling gives better results than maximum pooling and average pooling. The optimal number of convolutional layer for this task is 5. In addition, GPU has a 175× acceleration in training set and a 122× acceleration in test set, compared to CPU platform.
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Acknowledgements
This paper is supported by Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Natural Science Foundation of China (61502254, 61602250), Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010, 16KJB520025), Natural Science Foundation of Jiangsu Province (BK20150983).
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Zhang, YD., Muhammad, K. & Tang, C. Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform. Multimed Tools Appl 77, 22821–22839 (2018). https://doi.org/10.1007/s11042-018-5765-3
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DOI: https://doi.org/10.1007/s11042-018-5765-3