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At present, crop disease recognition is mostly realized based on convolutional neural network. However, due to the lack of training data in actual agricultural production, these crop disease recognition methods based on convolutional neural network often have limited applications and perform poorly. In order to carry out the low-cost, general and flexible crop disease recognition, a fewshot crop disease recognition network based on progressive learning and enhanced prototype metric was proposed. Specifically, an enhanced prototype metric module was firstly designed to compute the enhanced prototype that can accurately represent the category center, and make full use of its rich category information to recognize the crop disease. Then, a progressive learning strategy was designed to train the model to help it better adapt to the crop disease recognition, and further improve the few-shot crop disease recognition accuracy. On the self-made few-shot crop disease datasets FSCD-Base, FSCD-Complex and the cross-domain setting from FSCD-Base to FSCD-Complex, the 5-way 1-shot average recognition accuracy of the FPE-Net reached 70.65%, 53.47% and 49.58%, and the 5-way 5-shot average recognition accuracy of the FPE-Net reached 83.02%, 66.15% and 64.21%, respectively. These experimental results showed that the FPE-Net was significantly better than other fewshot crop disease recognition models, which can recognize crop diseases more accurately when the training data was insufficient.