Abstract: Abstract: Apples in China accounts for more than 50% of the global production and consumption at present. However, the quantity and quality of apples have been threaten by the various diseases, such as alternaria boltch, brown spot, mosaic disease, gray spot, and rust. The CNN-based methods can be expected to recognize the crop leaf disease for the high recognition rates. But, there is still lacking on the recognition accuracy, due mainly to the lack of linkage between global and patch features of disease images in the general disease recognition models. In this study, a disease recognition model was proposed using the patch and global features interactively coupling model (GPF-IC). The main characteristics were also addressed for the small inter-class and large intra-class differences in the apple leaf disease images under natural conditions. Firstly, an attention fusion module was designed in the global feature extraction branch. The convolutionally extracted feature maps were then enhanced to fuse the information on the channels and spaces. The global features and attention activation maps were generated from the enhanced feature maps. Secondly, a cropping module was designed to crop the original image using the attention activation maps. The blocks of images were obtained with the disease information in the patch feature extraction branch, particularly with the patch features. Thirdly, the multi-head cross-attention feature coupling module was designed to realize the bi-directional cross-coupling of patch and global features. As such, the recognition accuracy was improved to enhance the representation capability of fine-grained features of disease images. Finally, three operations of data enhancement were used to evaluate the learning effect of the model for the less overfitting, due to the insufficient training data. A total of 30 540 disease images of six types of apple leaves were obtained with the sufficient number of samples and balanced distribution. The improved model was included as follows. 1) The global feature extraction branch was proposed to promote the disease recognition accuracy by 0.79 percentage points using the attention fusion module. 2) A patch feature extraction branch and a cropping module were introduced to implement the local feature extraction. The model accuracy was then improved by 1.22 percentage points than before. 3) A multi-head cross-attention coupling module was proposed to couple the features from the different branches for the feature extraction and expression capability of the model. The recognition accuracy was improved by 3.39 percentage points, which was the highest recognition accuracy of 98.23%. The experiment demonstrated that the feature extraction can effectively exclude the non-target noises to locate the most discriminative region using the global feature extraction branch. The patch feature extraction branch was efficiently acquired the patch information using the image block embedding. The feature coupling module was realized the interactive coupling of global and patch tokens for the better fine-grained feature representation using multi-headed cross-attention. The GPF-IC was achieved in the 98.23% recognition accuracy of apple leaf disease. The finding can provide a technical support for the automatic recognition of apple leaf diseases in natural scenes.