Chinese Herbal Medicine Classification Using Convolutional Neural Network with Multiscale Images and Data Augmentation Correct use of Chinese herbal medicines is vital to life safety of the patients. Chinese herbal medicine classification is very important for the correct use of Chinese herbal medicines. Traditional methods like microfeature identification and physiochemical identification are inefficient due to the various kinds and different conditions of Chinese herbal medicines. Therefore, we adopt a multiscale convolutional neural network (CNN) model with data augmentation technology to classify Chinese herbal medicines. The data augmentation techniques solve the problem of less data on Chinese herbal medicines. Multiscale technology extracts more useful features for Chinese herbal medicine classification. The experiments show favorable accuracy.

Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network In order to identify and prevent tea leaf diseases effectively, convolution neural network (CNN) was used to realize the image recognition of tea disease leaves. Firstly, image segmentation and data enhancement are used to preprocess the images, and then these images were input into the network for training. Secondly, to reach a higher recognition accuracy of CNN, the learning rate and iteration numbers were adjusted frequently and the dropout was added properly in the case of overfitting. Finally, the experimental results show that the recognition accuracy of CNN is 93.75%, while the accuracy of SVM and BP neural network is 89.36% and 87.69% respectively. Therefore, the recognition algorithm based on CNN is better in classification and can improve the recognition efficiency of tea leaf diseases effectively.