# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
###### Abstract  
With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet01) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model. g +
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objects using methods like thresholding, region growing, and edge detection. In recent years, computer vision technology, especially convolutional neural networks (CNN), has achieved some satisfactory results in plant disease identification, when plant disease identification networks trained in the training dataset are applied in the test dataset in the case of a training dataset and test dataset all from the same domain [3]. However, the image distribution of the grape leaf diseases dataset collected in the fields is domain migration due to some factors such as different varieties, growth periods, locations, weather, etc. [4]. It leads to the grape leaf diseases identification network being trained on the one grape leaf diseases domain dataset that performs poorly on the other grape leaf diseases domain dataset, namely generalization problem in plant disease identification. Therefore, it is necessary to propose a grape leaf disease identification network with strong generalization ability to solve weak generalization due to domain migration. Aiming at the weak generalization ability of grape leaf disease recognition, the research methods based on deep learning include data enhancement [5], model fine-tuning [6], and domain adaptation. The first method is to expand the dataset of rice leaf disease by data enhancement (such as flipping, translating, building an antagonistic network, etc.) to enhance the diversity of the original dataset of grape leaf disease. The recognition network trained on this kind of dataset of grape leaf disease learns richer data features and prevents over-fitting, thereby enhancing the generalization ability of the recognition network. However, the data on grape leaf diseases collected by the recognition network are not consistent with the data of the source domain, in other words, the accuracy of the recognition network will decrease when the data of the source domain are inconsistent with the data of the target domain, that is, the generalization ability will not improve significantly. The second model is based on the original grape leaf disease dataset, which is trained in the new grape leaf disease dataset to adjust the weights and parameters of the network. However, model fine-tuning can only improve the generalization ability on a single grape leaf disease dataset and needs to be trained again when applied to other fields.