Abstract
In view of the obvious differences in the manifestations of the same diseases in apples at different stages of the disease, different diseases show certain similarities, and the early symptoms of the disease are not obvious. For these problems, a new model attention residual network (ARNet) was introduced based on the combination of attention and residual thought. The model introduces the multi-layer attention modules to solve the problems of early disease location dispersion and features that are difficult to extract. In order to avoid network degradation, a residual module was constructed to effectively integrate high and low-level features, and data augment technology was introduced to prevent the model from over-fitting. The proposed model (ARNet) achieved an average accuracy of 99.49% on the test set of 4 kinds of apple leaf diseases with real complex backgrounds. Compared with the models ResNet50 (99.19%) and MobileNetV2 (98.17%), it had better classification performance. The model proposed in this paper had strong robustness and high stability and can provide a reference for the intelligent diagnosis of apple leaf diseases in practical applications.
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