Automatic recognition of crop leaf diseases is an important application of computer vision technology in agriculture. In recent years, deep learning methods have made some progress in the recognition of crop leaf diseases, and they are all based on deep feature representations of a single deep convolutional neural network (CNN) model. However, the useful fact that the image representation ability of different deep CNN models is complementary has not received attention for research. Thus, this study proposes a network model MDFF-Net for fusing different deep features. MDFF-Net connects two pre-trained deep CNN models in parallel and then sets a fully connected layer with the same number of neurons for each model to transform the deep features output by different models into features with the same dimension. Then, through the non-linear transform of two fully connected layers, the effect of feature fusion is further improved. We choose VGG-16 and ResNet-50 as the feature extractors of MDFF-Net and conduct experiments on a public dataset containing five apple leaf diseases. The experimental results show that the recognition accuracy of MDFF-Net is 96.59%, which is better than the results achieved by VGG-16 or ResNet-50 alone and thus proves the effectiveness of the deep feature fusion method.