Abstract: With the popularity and rapid development of deep learning applications, image recognition methods based on deep learning are widely used in the field of crop diseases and insect pests. However, most neural networks attach importance to the improvement of recognition accuracy, but ignore the huge parameter computation amount of neural networks. In order to solve this problem, based on the progressive growing of GANs discriminator model and convolutional attention module. an improved CPDM network model was proposed to identify crop pests and diseases. By adjusting the network structure of the progressive growing of GANs discriminator, the feature extraction capability of CPDM network model was enhanced by using balanced learning rate, pixel-level feature vector normalization and convolutional attention module, and the recognition accuracy of real images was improved. The experiment was carried out on the PlantVillage dataset, and compared with VGG16, VGG19 and ResNet18, the TOP-1 accuracy was 99.06%, 96.50%, 96.65% and 98.86%, respectively, which was improved by 2.56%, 2.41% and 0.2%, respectively. And the number of parameters was only 8.2 M. The experimental results show that the proposed CPDM network model meets the purpose of effectively controlling the calculation amount of neural network parameters on the basis of ensuring the classification accuracy.