# Data Augmentation Method for Plant Leaf Disease Recognition
###### Abstract
In this paper, we propose a data augmentation method based on image-to-image translation that can increase the sample diversity of diseased leaf datasets with insufficient numbers. The proposed augmentation method performs translation between healthy and diseased leaf images through the cycle-consistent generative adversarial networks (Cycle GAN) [16]. However, vanilla CycleGAN shows poor plant leaf image translation results, and we point out and improve on the following two problems: (1) poor reflection of the evident texture of the target disease, and (2) poor preservation of the shape of the input leaf image due to indiscriminate transformation in the background region. To solve this problem, we further utilize an Attention mechanism and a binary mask that explicitly indicates the location of the leaves. The attention mechanism is a method of selectively focusing on key information within given information and can greatly improve the expressive ability of the proposed plant leaf translation model. In deep learning, attention mechanisms originated in human perceptual systems as a method of selecting key information to focus on [17; 18]. Many studies combining attention mechanisms have been conducted, and it has been demonstrated that the model can significantly improve the feature expression ability [17; 18; 19; 20; 21]. We used the Convolutional Block Attention Module (CBAM) [22] to construct a network that allows the generator to decide which features should be preferentially selected to deceive the discriminator. CBAM is a lightweight attention module that can learn attention maps corresponding to spaces and channels from a given feature map. We use CBAM to configure the generator network as a Pre-Activation Residual Attention Network and perform adaptive feature refinement during the training process. Also, we train a background loss function that regulates the generator to not perform unnecessary transformations on background regions. The background loss suppresses unnecessary transformations between the input data and the transformed image through a binary mask explicitly representing the background region of the input image. This assists the attention module in quickly focusing on the leaf itself, excluding the background where it cannot perform a transformation. 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. We conducted experiments using apple, potato, and grape leaf data provided by the Plant Village [23] dataset and verified whether the proposed method can solve the problems of images produced by vanilla CycleGAN. We then compared the experimental results of various classification models to verify whether the extended dataset from the images generated by the proposed method can improve performance. Experimental results prove that the proposed augmentation method can solve the problem of lack of diversity in some disease samples that are difficult to collect by generating usefully translated images and contributing to improving classification performance. The main contributions of this study are as follows:  
* In this study, we focus on the overfitting problem caused by imbalanced data problems created by some disease images that are rarely observed in natural environments and propose a novel plant image augmentation method based on CycleGAN to solve this problem. * We point out two issues that vanilla CycleGAN needs to address for image augmentation: (1) poor reflection of the evident texture of the target disease, and (2) poor preservation of the shape of the input leaf image due to indiscriminate transformation in the background region. * To improve the poor image conversion results of CycleGAN, we utilized the attention mechanism and binary mask, and solved the bias problem of classification models by constructing an extended dataset.