Rubber Leaf Disease Recognition Based on Improved Deep Convolutional Neural Networks With a Cross-Scale Attention Mechanism - Zeng, Tiwei; Li, Chengming; Zhang, Bin; Wang, Rongrong; Fu, Wei; Wang, Juan; Zhang, Xirui (Frontiers in Plant Science, 2022)
Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, symptoms and spots of diseases at different stages and scales with complex morphological characteristics in practice, and there are subtle interclass difference and large intraclass variation between the symptoms of diseases. In order to tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution module(GMDC) for multi-scale feature extraction as well as a cross-scale attention feature fusion module(CAFF) for multi-scale attention feature fusion. Specifically, The model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model’s adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional feature of multi-scale diseases, strengthen the disease features fusion at different scales. In this paper, A dataset of rubber leaf diseases was constructed, including 2788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65M, and the model size is only 5.62MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but also the recognition accuracy is improved by 3.86%-6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.