A method suitable for identifying and classifying pepper diseases was designed to achieve precise recognition and classification of pepper diseases in complex environments including background clutter or interference.Six common diseases in the growth process of peppers were used to expand the dataset with data augmentation methods.An improved MaxViT-DF model was proposed based on MaxViT.The ordinary convolution in the MaxViT model was replaced with deformable convolution to enable the model to extract features closer to the recognition target in complex environments.A feature fusion module was introduced when applying attention to the MaxViT model to improve the model’s global perception ability.The results showed that the improved MaxViT-DF model had an identification and classification accuracy of 98.10%，and the classification accuracy for six common pepper diseases was higher than 95%.The improved model had significant advantages in convergence speed and classification accuracy compared with models including ResNet-34，EfficientNetv2，and VGG-16.It is indicated that the MaxViT-DF model can effectively identify and classify common diseases in different types of peppers.