Apple leaf disease identification based on improved convolutional neural network
To solve the problem that the recognition accuracy of the traditional convolutional neural network in the agricultural field is not high,the existing VGG16 network model is improved to solve the problem of the identification and prediction of apple leaf diseases.The network obtains the feature map through convo-lution operation towards the input image,and adds mass normalization module after each convolutional layer is completed to improve the convergence speed of the model.At the same time,the ReLU activation function of the convolutional layer is replaced with the P-ReLU function to improve the efficiency of network training and solve the problem of gradient disappearance.The experiment result in the published plantvillage dataset is 98.578％,which indicates that the improved method in this paper has better disease classification and prediction accuracy than some classical network models.
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万方数据
