# Data Augmentation Method for Plant Leaf Disease Recognition
###### Abstract  
Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set. plant disease recognition; data augmentation; imbalanced dataset; convolutional attention +
Footnote †: journal: Applied  
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conducted an experiment to classify 26 disease images from 14 crop species using Google LeNet and AlexNet models and reported an average accuracy of 99.3%. [PERSON] et al. [11] also pointed out that models taken on datasets generated in controlled experimental environments achieved 99.5% accuracy when tested with models AlexNet, AlexNetOWTBn, GoogLeNet, OverFeat, and VGG, but did not perform the same in real-world environments. The accuracy dropped to 33% when using the learned model in the real world. [PERSON] et al. [12] conducted a study on image-based plant disease recognition using the Shallow VGG network. The Shallow VGG network is constructed using only a few layers from a pre-trained VGG network and serves to perform feature extraction from input data. The authors proposed to classify the features extracted through this through the Random Forest and Xgboost models. As a result of the experiment, it was reported that the two combined models proposed have better classification performance than the Vanilla VGG Network model. [PERSON] et al. [13] performed image-based plant leaf disease research based on Recurrent Neural Network. The authors point out that CNN-based classification models do not clearly capture disease areas, and to solve this problem, they proposed a network combining Gated Recurrent Units (GRU) and Attention modules. They verified through experiments that the proposed method can capture clear disease regions unlike CNN and reported that classification performance was improved. Although many vision-based plant disease recognition advanced studies using deep learning are in progress, some diseases are not common in nature, making it difficult to collect the same amount of data from healthy plant samples. Therefore, datasets for plant disease diagnosis collected from natural environments often lack samples of these rare disease classes, a problem known as imbalanced data in machine learning. When trained from such imbalanced data, most supervised learning models suffer from the overfitting problem, in which decision boundaries are biased toward major classes [14; 15]. To solve this problem, previous work has adopted a manual augmentation method using professional manpower, but this is very inefficient and has high costs.