# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
###### Abstract  
The lack of large balanced datasets in the agricultural field is a glaring problem for researchers and developers to design and train optimal deep learning models. This paper shows that using synthetic data augmentation outperforms the standard methods on object detection models and can be crucially important when datasets are few or imbalanced. The purpose of this study was to synthesize rice leaf disease data using a Style-Generative Adversarial Network Adaptive Discriminator Augmentation (SG2-ADA) and the variance of the Laplacian filter to improve the performance of Faster-Region-Based Convolutional Neural Network (faster-RCNN) and Single Shot Detector (SSD) in detecting the major diseases affecting rice. We collected a few unbalanced raw samples of rice leaf diseases images grouped into four diseases namely; bacterial blight (BB), tangro (TG), brown-spot (BS), and rice-blast (RB) with 1584, 1308, 1440, and 1600 images, respectively. We then train StyleGAN2-ADA for 250 epochs whilst using the variance of the Laplacian filter to discard blurry and poorly generated images. The synthesized images were used for augmenting faster-RCNN and SSD models in detecting rice leaf diseases. The StyleGAN2-ADA model achieved a Frechet Inception Distance (FID) score of 26.67, Kernel Inception Distance (KID) score of 0.08, Precision of 0.49, and Recall of 0.14. In addition, we attained a mean average precision (mAP) of 0.93 and 0.91 for faster-RCNN and SSD, respectively. The learning curves of loss over 250 epochs are 0.03 and 0.04 for Faster-RCNN and SSD, respectively. In comparison to the standard data augmentation, we achieved a \(t\)-test \(p\)-value of \(9.1\times 10^{-4}\) and \(8.3\times 10^{-5}\). Hence, the proposed data augmentation pipeline to improve faster-RCNN and SSD models in detecting rice leaf diseases is significant. Our data augmentation approach is helpful to researchers and developers that are faced with the problem of fewer imbalanced datasets and can also be adopted by other fields faced with the same problems. data augmentation; object detection models; rice leaf disease detection; StyleGAN2-ADA; laplacian filter 2023131346. [[https://doi.org/10.3390/app13031346](https://doi.org/10.3390/app13031346)]([https://doi.org/10.3390/app13031346](https://doi.org/10.3390/app13031346))  
Article  
An Improved Approach to Detection of Rice Leaf GAN-Based Data Augmentation Pipeline  
[PERSON], Y.  
in large fields where visual detection affects the spatial estimation of disease spread for control. The recent advancement in computer vision (CV) has made it possible to detect plant leaf disease using machine learning (ML) models. Over the decade, there have been numerous studies using various CV algorithms to detect rice leaf diseases, such as image processing, support vector machine (SVM), image classification, object detection, and pattern recognition with various degrees of success [7; 8; 9; 10; 11; 12; 13; 14]. [PERSON] & [PERSON] [15] proposed a system for detecting rice leaf diseases using a Neighbor and Minimum Distance classifier (k-Nearest) while [PERSON] et al. [10] used an SVM and deep learning (DL) to detect rice leaf disease. A more advanced approach is the use of object recognition and detection algorithms. [PERSON] et al. [8] used Faster-Region-Based Convolutional Neural Network (faster-RCNN), RetinaNet, You Only Look Once (YOLOv3), and Mask-Region-Based Convolutional Neural Network (Mask-RCNN) detection models to detect rice leaf diseases using 6330 training images with an accuracy of 75.92%, 70.96%, and 36.11%, respectively. In addition, [PERSON] et al. [14] used a transfer learning (TL) approach to detect and classify various rice leaf diseases using DenseNet and Inception models with 500 images. However, these existing methods could be optimized to achieve better efficacy for detecting crop disease. It is quite evident that most researchers use quite a few datasets for training deep neural networks, which as a result affects the model generalization and detects poorly in real scenarios.