Aiming at the problem that traditional crop disease recognition methods rely on manual extraction of features, the steps are complex and inefficient, and it is difficult to recognize in the field environment, a crop disease recognition model combining multi-scale convolution structure and attention mechanism was proposed. This research improved on the basis of residual network (ResNet18), introduced the Inception module, and its multi-scale convolution kernel structure was used to extract disease features at different scales, and the richness of features was improved. On the basis of the residual structure, the attention mechanism squeeze-and-excitation networks (SE-Net) was added to enhance the weight of useful features, weaken the influence of useless features such as noise, and further improve the feature extraction ability and enhance the robustness of the model. The experimental results showed that the improved multi-scale attention residual network model (Multi-Scale-SE-ResNet18) had an average recognition accuracy of 95.62% on the eight crop disease data sets collected in a complex field environment, compared with the original accuracy of the ResNet18 model, it was increased by 10.92 percentage points. The model size was only 44.2MB. The improved Multi-Scale-SE-ResNet18 had better feature extraction capabilities, and it can extract more disease feature information, and better balance the recognition accuracy of the model with the model complexity, which can be used for crop diseases identification in the field environment.