Bacterial blight of soybean (BBS), caused by Pseudomonas syringae pv. glycinea, is one of the most devastating diseases in soybean with significant yield losses ranging from 4% to 40%. The timely detection of BBS is the foundation for disease control. However, traditional identification methods are inefficient and rely heavily on expert knowledge. Existing automated approaches have not achieved high accuracy in natural environments and often require advanced equipment and extensive training, limiting their practicality and adaptability. To overcome these challenges, we propose LeafDPN, an improved Dual-Path Network model enhanced with Vision Transformer blocks in the forward propagation function and SE blocks in the ConvBNLayer. These enhancements improved the model’s accuracy, receptive field, and feature expression capabilities. Experiments conducted on a self-constructed dataset of 864 expert-labeled images across three disease types demonstrated that LeafDPN achieved a 98.96% identification accuracy and the shorted iteration time in just 24 epochs. It outperformed 14 baseline models like HRNet and EfficientNet in terms of accuracy, training efficiency, and resource consumption. In addition, the proposed LeafDPN model has the potential to be applied in the identification of other plant diseases based on available datasets.