关键词: 水稻, 病害识别, EfficientNet, 注意力模块, Adam

Abstract: To achieve fast and accurate recognition of rice disease images, a lightweight rice disease recognition method based on an attention mechanism and EfficientNet is proposed. We used the convolutional block attention module as the lightweight attention module to improve the mobile inverted bottleneck convolution of the main module in EfficientNet-B0. Then the Ghost module is used to optimize the convolution layer of the network to reduce the number of parameters and computation time of the network. Finally, the Adam optimization algorithm is used to improve the convergence speed of the network. On the test set, which contained 572 rice images of rice bacterial blight, rice kernel smut, rice false smut, rice helminthosporium leaf spot, and healthy leaves, the recognition accuracy of the proposed method was 95.63%. This was 1.75% higher than the recognition accuracy of EfficientNet-B0 on the same test set, and 8.39%, 4.72%, 3.67%, and 1.05% higher than the recognition accuracy of similar classical neural networks VGG16, Inception-V3, ResNet101, and DenseNet201, respectively. The number of parameters of the proposed lightweight rice disease recognition method was 4.4 M, which was 2.8 M less than that of EfficientNet-B0. Compared with the comparison network, the number of parameters of the proposed method was 9.05%, 18.37%, 9.81%, and 21.64% of them. Our results show that the proposed method can identify different rice disease images accurately and quickly.

Key words: rice, disease recognition, EfficientNet, attention module, Adam