# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 2 Relative Works and Challenges  
The objective of this section is to understand the research progress and developmental trends of generative models and some key issues while also illustrating the various DL models that have adopted the GANs to generate augmented images and the significance of the augmentation process. Since the introduction of GAN architecture by [PERSON] et al. [23], many related architectures have subsequently been proposed with some impressive results. While Deep Convolutional Generative Adversarial Network (DCGAN) and Conditional Generative Adversarial Network (cGAN) could be referred to as extensions [24]. The advanced GANs are Wasserstein Generative Adversarial Network (WGAN), Big Generative Adversarial Network (BigGAN), Cycle-Consistent Generative Adversarial Network (CycleGAN), Style-Based Generative Adversarial Network (SG), and SG2-ADA [25]. The DCGAN extends the foundational GAN by implementing Convolutional Neural Network (CNN) settings which provides stability to the training process but bring about an issue of mode collapse whereby the model produces a single type or small set of output [26]. In addition, the DCGAN's have a vanishing gradient, i.e., when the generator fails to learn due to information starvation which results in a poor generator and a robust discriminator [27]. WGAN resolved the issues of mode collapse, vanishing gradient, and stability during training but prolonged the training session and, at times, produced poor output [28]. The BigGAN offers more stability during training and has better results than WGAN but requires more extensive data samples and time [29; 30]. SG architecture provides an advanced solution by improving the traditional GAN architectural model and training process by redesigning the generator normalization, modifying progressive growth, and regularizing the generator [31]. They provided an advanced method for high-resolution image synthesis, but sometimes they generate uneven parts inside an image and require a considerable amount of quality datasets [32]. SG2 traces the unnatural blob-like artifact caused by SG by stabilizing the high-resolution training; moreover, using small datasets leads to discriminator overfitting [33]. The SG2-ADA produces excellent results with a small dataset while achieving a better result [34]. In addition, SG2-ADA has been adopted in augmenting training datasets in various fields of DL applications, such as generating images for facial recognition, medical image synthesis for a brain tumor, generating synthetic datasets for liver lesion classification, generating images for manufacturing components, and generating datasets for credit card fraudulent transaction predictions [35; 36; 37; 38; 39]. Recently, studies have been aimed at automating plant leaf disease detection based on DL methods with various degrees of success using different model architectures and pre-trained models, such Fast-RCNN, Faster-RCNN, Region-based Convolutional Neural Networks (R-CNN), Histogram of Oriented Gradients (HOG), YOLO, Region-based Fully Convolutional Network (R-FCN), Single Shot Detector (SSD) and Spatial Pyramid Pooling (SPP-net) [40]. However, a common problem in the field of plant leaf disease detection is the lack of sufficient and diverse training datasets, which often leads to an undesirable effect on the model's performance while making the model run into an overfitting problem because of the way DL models learn, i.e., DL model requires a considerable amount of data to perform well [41]. The standard augmentation methods artificially expand the same training datasets by modifying their positions and colors to create more data while the GANs create a new set of diverse data. Hence, the former method does not impact much on the model generalization because it is not introducing new datasets to the object detection model but instead modifies their positions and colors [19; 42], this point proves to be significant in improving the accuracy and generalization of DL models.