Abstract: Aiming at the problems of slow speed, subjectivity, high requirement of professional knowledge and high recognition cost in the identification methods of crop pests and diseases in China, a crop pest and disease identification method is proposed based on Inception-resnet-v2 network model combining Inception and Residual structure. To achieve accurate and efficient crop pest identification, the network uses a residual structure to combine low-level features with high-level features to increase network depth. At the same time, the Inception structure is added, which not only maintains the sparsity of the network structure, but also utilizes the high computational performance of the dense matrix to speed up the training. Finally, the multi-class prediction is performed by the Softmax classifier. Compared with the traditional method, the proposed method converges faster, not only with an accuracy rate of 96.67%, but also with an accuracy of 90.77% and a recall rate of 89.72%. It also makes the different categories of pest and disease identification more balanced, improving the specific methods in the traditional methods, by which the recognition effect is poor.

Key words: crop pest recognition, Inception structure, Residual structure, Inception-resnetl-v2, Softmax classifier