Rice disease segmentation method based on CBAM-CARAFE-DeepLabv3+

Rice is an important food crop, but it is susceptible to diseases during its growth process. Rapid, accurate, and effective identification of rice diseases is important for targeted measures to control disease spread. It is crucial for improving rice yield and quality. Therefore, this study proposes a CBAM-CARAFE-DeepLabv3+ rice disease segmentation method that combines attention mechanisms and feature recombination. This method focuses on three common diseases in rice growth: bacterial blight, blast, and brown spot disease. To enhance the extraction of favorable features, the algorithm adopts CBAM-RepViT as the backbone network. That is, the Squeeze-and-Excitation (SE) attention mechanism embedded in the efficient and lightweight RepViT network is replaced by the lightweight Convolutional Block Attention Module (CBAM). Compared to the SE attention mechanism, CBAM introduces a spatial attention module that focuses on important spatial positions in the feature map. It allows the model to extract more rich and detailed feature information by attending to both the channel and spatial dimensions of the feature map. Additionally, to further improve the feature extraction ability and image edge segmentation accuracy during upsampling, the lightweight Content-Aware ReAssembly of FEatures (CARAFE) operator is introduced into the decoding module for upsampling. Finally, to address the issue of imbalance between foreground and background pixel ratios in rice disease, a hybrid loss function composed of cross-entropy (CE) loss and Dice loss is proposed. Experimental results show that, compared to other networks such as DeepLabv3+, the proposed CBAM-CARAFE-DeepLabv3+ method achieves further improvement in segmentation accuracy, providing a new method for the development of rice disease segmentation technology.