Classification of Chinese Herb Images Using Deep Learning Technique via Android Application
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Abstract
Currently, the herb-based health-care has been receiving more attention, especially Chinese herbs that are able to alleviate or treat diseases. However, examining the type and remedial effect of Chinese herbs remains difficult for the general public. This research aimed to develop an android application for classification of Chinese herb images with deep learning technique. The research started from preparation of 2,640 test images for processing. The next step was taking the collected images to be used as a training set for training the model, based on deep learning, convolutional neural network by using Tensorflow’s library for image classification. This model was trained to be able to recognize 12 Chinese herbs. Then, the model was developed into an application. The last step was application performance testing. The statistics used in this research were the accuracy, precision, recall, mean, and standard deviation. The research found that (1) the deep learning model for Chinese herb classification at 500 cycles had the maximum accuracy of 98.30%, precision of 98.29% and recall of 98.30%; 2) the results showed that average accuracy of the application for Chinese herb image classification was 89.17%; and (3) performance assessment by experts had high suitability, and the results of the user satisfaction of the application was at a high level with an average of 4.23. The experimental results indicate that the developed application can effectively help classify Chinese herb image.
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