Abstract:[Purposes] To address the problems of high parameter quantities and gradient disappearance in existing few-shot learning for summarizing crop disease recognition, a multi-branch grouped convolutional feature cascade few-shot learning model for crop disease image recognition is proposed. [Methods] Firstly, the model applies grouped convolution to the residual block, using the residual block to alleviate the gradient disappearance problem of the network. Secondly, the input of the network is split into isomorphic multi-branch embeddings, and the feature supplement between different branches is used to obtain more rich features and reduce the number of network parameters at the same time. Finally, a multi-layer feature cascade backbone network is designed to mine information at different levels in the image, cascading the output of more location, detail information in shallow features and stronger semantic information in deep features to improve the generalization ability of the network. [Finding] In the PlantVillage dataset, the accuracy rates of 5-way 1-shot and 5-way 5shot are 79.68±0.35 and 93.25±0.21, respectively, which are 3.74% and 4.32% higher than the prototype network. [Conclusions] The model in this paper has a certain performance improvement in the effectiveness of crop disease identification, and realizes the functions of deepening the network depth while reducing the amount of network parameters as much as possible, alleviating gradient disappearance, and mining semantic information at different levels, which can effectively solve the problem of effective samples of crop diseases. fewer problems.