Identification method of small sample tea leaf diseases based on attention mechanism and transfer learning
In order to improve the accuracy of tea disease recognition,a recognition method of small sample tea disease image based on attention mechanism and transfer learning was proposed.Firstly,the training set samples were expanded by random rotation,random flip,random color and random brightness adjustment operations to reduce the overfitting risk of the network model.Secondly,Convolutional Block Attention Module(CBAM)was introduced to optimize the ResNet50 network model so that it could focus more accurately on the characteristics of tea diseases.Finally,the ResNet50 model was pre-trained with the help of plant disease data set,and the pre-trained model parameters were transferred to the tea disease data set for training.The experimental results show that the recognition accuracy of the expanded data set is 7.85%higher than that of the original data set,the recognition accuracy is 3.26%higher after two transfer learning,and the recognition accuracy is 0.81%higher after adding CBAM attention mechanism.Compared with the original model,the sample recognition rate of the proposed method is increased from 77.24%to 89.16%when 8 tea tree diseases are verified.The improved model has better feature extraction ability,which can effectively improve the recognition accuracy of tea disease in small samples.
NETL
NSTL
万方数据
