Ensuring the quality and yield of rice depends heavily on the accurate identification of early-stage rice diseases. Existing models face significant challenges in balancing lightweight requirements and precise classification of rice disease types due to the noisy background and scattered distribution of disease symptoms in real-world environments. To address the above issues, this study proposes DGLNet, a novel lightweight and highly accurate network for rice disease identification. DGLNet includes two low-complexity modules, the global attention module (GAM) and the dynamic representation mod...
