  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 over-fitting. 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.

Computer Vision and Pattern Recognition;Machine Learning