Abstract: Accurate identification of maize leaf diseases plays a crucial role in preventing crop diseases and improving maize yield. However， plant leaf images are often affected by various factors such as complex backgrounds， climate conditions， lighting， and imbalanced sample data. To enhance recognition accuracy， this study proposes a lightweight convolutional neural network named ICS-ResNet， which is based on the ResNet50 backbone network and incorporates improved spatial and channel attention modules along with depthwise separable residual structures. The residual connections in the ResNet architecture prevent gradient vanishing during deep network training. The improved channel attention module （ICA） and spatial attention module (ISA) fully leverage semantic information from different feature layers to precisely localize key network features. To reduce the number of parameters and computational costs， traditional convolution operations are replaced with depthwise separable residual structures. Additionally， a cosine annealing learning rate strategy is employed to dynamically adjust the learning rate， mitigating training instability， enhancing the model's convergence ability， and preventing it from getting trapped in local optima.Finally， experiments were conducted on the Corn dataset from PlantVillage， comparing the proposed lightweight network with six other popular networks， including CSPNet， InceptionNet_v3， EfficientNet， ShuffleNet， and MobileNet. The results demonstrate that the ICS-ResNet model achieves an accuracy of 98.87%， outperforming the other six networks by 5.03， 3.18， 1.13， 1.81， 1.13， and 0.68 percentage points， respectively. Moreover， compared to the original ResNet50， the parameter size and computational cost are reduced by 16.27 MB and 2.25 GB， respectively， significantly improving the efficiency of maize leaf disease classification.

Key words:  , corn, leaf diseases, attention mechanisms, convolutional neural networks, depth-separable residual structure, image recognition