# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
###### Abstract
Moreover, DL models require considerable data to perform at an optimum rate, otherwise, they run into an overfitting problem and generalize poorly [16]. In addition, labeling a considerably large amount of plant leaf disease data is tedious and time-consuming. The absence of a diverse large dataset is a significant setback for developing a model that is fit to detect rice leaf disease. Previous studies have used standard augmentation methods to create random new samples to increase the size of the image dataset [17], while others used an adaptive method in generating new samples [18]. Moreover, these methods artificially expand the same training datasets by modifying their positions and colors without introducing new \"unique\" data to the model. Hence, the model has already seen these data but in a different state of position and color which does not impact much on the model generalization [19]. To enhance the diversity of the dataset and also to solve the problem of a small dataset, new data can be created using a Generative Adversarial Network (GANs). GAN is a generative ML framework that uses two neural networks, a generator, and a discriminator [20]. The generator generates a high-dimensional perceptual object from a latent space while the discriminator solves a classification task by distinguishing real objects from the input dataset from fake ones generated by the generator. Intuitively, the training sets an adversarial game between two players, then proceeds in three steps of each round and iterates as many rounds as required. Recently, the StyleGAN2 Adaptive Discriminator Augmentation (SG2-ADA) architecture performs better than most of GAN's methods and can generate high-quality objects with fewer training datasets and low computational cost by implementing ADA and TL, respectively [21]. Furthermore, the class imbalance issue is prevalent in plant leaf disease data [22] i.e., the sample data of one pathology may be far more widespread than other sample data which often results in biased models and there is a lack of studies that utilize SG2-ADA to generate a quality new set of data for rice leaf disease detection system. In this study, we propose a pipeline for synthetic augmentation of rice leaf disease dataset using SG2-ADA to increase the efficacy of deep neural networks in detecting rice leaf disease, we adopted the standard faster-RCNN and the Single Short Detector (SSD) as our deep convolutional neural networks. While GAN augmentation takes more time and resources compared to the standard augmentation techniques, our work shows that the GAN technique can improve the efficacy of the rice leaf disease detection model better than the standard approach, which is central in high-stake agricultural decision-making. Furthermore, there have been several studies conducted to examine the efficacy of GAN-based data augmentation, albeit these studies are few in contrast to the effectiveness of the GAN approach over the standard augmentation approach. We aim to fill the research gap by exploring the problem of small datasets and class disease imbalances by evaluating the performance of standard and GAN-based data augmentation methods. Hence, to the best of our knowledge, this is the first work that uses SG2-ADA to synthetically augment the rice leaf disease dataset in order to enhance the performance of rice leaf disease detection models.