Maize is an important food crop, and accurate identification of maize leaf diseases is significant for disease control. Aiming at the complexity of the color and texture features of diseases with inter-class similarity and intra-class variability, a two-channel multi-scale network model with a cross-attention mechanism and spatial dimension feature fusion (CASF-MNet) was designed. Firstly, aiming at the complexity of color features, the HSV color Sub-network (CSNet) was designed to extract the color features of disease spots. Then, aiming at the complexity of texture features, Texture Sub-Network (TSNet) and Cross Attention mechanism (CAM) were proposed to enhance the texture features of disease spots. Finally, to better fuse color and texture features, a cross-spatial feature fusion mechanism (FFM) was proposed. In the five-fold cross-validation of the self-built dataset, the average F1-Score and average Accuracy of CASF-MNet reach 97.17% and 97.02%. In the test experiments of two public datasets, the F1-Score of CASF-MNet reaches 98.13% and 98.71%, and the Accuracy reaches 97.44% and 98.67%, which are better than the seven most advanced leaf disease identification methods. It is proved that CASF-MNet is advanced and stable, and can assist disease detection personnel in maize disease detection in field environments.