VMamba for plant leaf disease identification: design and experiment

Experimental results demonstrate that the VMamba model outperforms mainstream models such as ResNet50, Vision Transformer, and Swin Transformer in disease recognition accuracy, whether on large-scale datasets like PlantVillage or optimized small-sample disease datasets, showcasing superior performance. Compared to Swin Transformer, VMamba achieves a 3.49% increase in accuracy while reducing training time by 80%. Furthermore, the proposed DDHTLVMamba training method demonstrates its effectiveness on small-sample datasets, significantly reducing pre-training time while maintaining recognition accuracy comparable to that achieved with large-sample transfer learning. This study provides an innovative approach for the efficient identification of agricultural diseases and is expected to advance the development of intelligent agricultural disease prevention and control technologies.