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Crop disease recognition is a prerequisite for rational pesticide application and a powerful guarantee for promoting healthy and stable agricultural development. Existing deep learning-based crop disease recognition methods mainly use classical networks such as VGG and ResNet or networks that use attention mechanisms for disease recognition. Although these deep learning-based crop disease recognition methods have achieved better disease recognition results than traditional methods, they do not sufficiently mine the discriminative information contained in the shallow, middle and deep features of networks, and most of the extracted saliency features of crop disease images are insufficient. To extract discriminative features in crop disease images more effectively and improve crop disease recognition accuracy, a crop disease recognition network based on multi-layer information fusion and saliency feature enhancement (MISF-Net) was proposed. Specifically, MISF-Net mainly consisted of a ConvNext backbone network, a multi-layer information fusion module (MIFM), and a saliency feature enhancement module (SFEM). The ConvNext backbone network was mainly used to extract features of crop disease images. The multi-layer information fusion module was mainly used to extract and fuse the discriminative information from the shallow, medium and deep layers of the backbone network. The saliency feature enhancement module was mainly used to enhance the saliency discriminative features in crop disease images. The experimental results on the crop disease dataset AI challenger 2018 and the homemade dataset RCP-Crops showed that the crop disease recognition accuracies of MISF-Net reached 87.84% and 95.41%, and the F1 values reached 87.72% and 95.31%, respectively.