Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network
This work addresses tea leaf disease identification for agricultural applications, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of identifying tea leaf diseases by applying a convolutional neural network (CNN) to image recognition, achieving a recognition accuracy of 93.75%, which outperformed SVM (89.36%) and BP neural network (87.69%).
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.