In recent years, the application of deep learning algorithms in the field of image recognition has gradually expanded into agricultural production, particularly in the area of crop disease detection. Leveraging transfer learning techniques within deep learning, a method for identifying corn leaf diseases based on an improved MobileNetV3 model has been proposed. Pre-trained weights from the ImageNet dataset were transferred to the target dataset, and the model was further optimized. During the optimization process, the original SE (Squeeze-and-Excitation) module was replaced with a CBAM (Convolutional Block Attention Module) attention module, and dilated convolutions were introduced into the convolutional layers to increase the receptive field. After training, an optimal model for corn leaf disease identification was obtained. After applying transfer learning, the model's accuracy on the training set increased from 96.30% to 98.20%, with an improvement of 1.9 percentage points. With further optimization, the accuracy reached 99.09%, demonstrating improved classification performance. This enhancement not only retains the lightweight characteristics of MobileNetV3 but also significantly boosts the performance of corn leaf disease identification.