Apple Leaf Disease Recognition Based on Attention Mechanics and Multi-Scale Feature Fusion

Early diagnosis and accurate identification of apple diseases play a major role in reducing growing costs and curbing economic losses. The diagnosis and identification of apple diseases are more difficult in the natural farming environment. A large amount of background noise in complex natural environments makes apple disease features relatively inconspicuous and makes the features of different diseases less distinguishable. A single-scale feature extraction network will be more difficult to extract effective information. In order to solve this problem, this paper proposes an apple leaf classification network based on attention mechanism and multi-scale feature fusion. First, the residual unit of ResNet50 is improved by replacing the second convolution in the residual unit with a pyramidal convolution modified by using dilated convolution to obtain multi-scale fused features. Then a channel attention module is added to the residual bypass to enhance the weighting of the disease features and improve the classification accuracy. The experiments in this paper first validate the role of the attention mechanism and pyramidal convolution separately and find that both improve the model performance. Then the combination of attention mechanism and pyramidal convolution is validated, and the optimized model has stronger noise immunity and the classification accuracy on the validation set is 94.96%. The results show that the optimized model has a better classification effect and higher robustness for apple leaf disease pictures in the natural environment.