# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 6 Concluding Remarks  
The recent advancement in DL methods has made it possible to efficiently detect rice leaf disease automatically, albeit a large, diverse, and balanced dataset is required to achieve an optimum result. Currently, there are few balanced rice leaf disease datasets available in open source when compared to millions of other datasets freely available. Previous studies have used the standard augmentation methods to create new samples to increase the size of the image dataset by modifying the positions (rotation, etc.) and colors (brightness, etc.) without introducing \"new\" diverse data to the model which does not impact much on the model efficacy and generalization. We proposed a data augmentation pipeline using an SG2-ADA state-of-the-art generative model and variance of the Laplacian method to generate and filter out high-quality \"new\" synthetic rice leaf disease images to improve the performance of the CNN-based object-detection models. We compared the performances between the proposed augmentation pipeline and the standard augmentation method on one-staged and two-staged object detection models, SSD and Faster-RCNN, respectively. Based on our results we conclude that: (1) the proposed augmentation pipeline is efficient in producing high-quality synthetic images of rice leaf disease with few training datasets. (2) The visual quality of the synthetic images was very close to real images and could be difficult to visually distinguish. (3) We show that the proposed augmentation pipeline could be an effective tool for amplifying, diversifying, and correcting the imbalances in plant-based disease datasets. (4) We observed that the proposed augmentation pipeline yielded better efficacy when compared to the standard augmentation method. (5) Faster-RCNN and SSD model proves to be effective in detecting various classes of rice leaf disease. (6) This work skillfully combines several concepts, approaches, techniques, and components, such as Data Augmentation, Object Detection Models, Rice Leaf Disease Detection, SG2-ADA, Laplacian filter, Faster-RCNN, and SSD models. Hence, generative model-based augmentation is a promising area of study to improve model performance and to solve the issue of generalization when data is limited or imbalanced, though it comes with time and resource costs. This is true, especially in the area of agriculture, medicine, etc.  
Conceptualization, Y.H.; formal analysis, Y.H.; Funding acquisition, Y.H.; Investigation, Y.H.; Methodology, Y.H.; Software, Y.H.; Visualization, Y.H.; Writing (original draft), Y.H.; Validation, H.Y; project administration, S.Q.; supervision, S.Q.; Writing (review & editing), S.Q.; data curation, M.J.M.K.; resources, M.J.M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement:Data generated during the study are subject to a data sharing mandate and available in a public repository that does not issue datasets with DOIs. Rice Leaf Disease Data that support the findings of this study have been deposited in a GitHub repository [[[https://github.com/yunusa2k2/GANLapRice](https://github.com/yunusa2k2/GANLapRice)]([https://github.com/yunusa2k2/GANLapRice](https://github.com/yunusa2k2/GANLapRice))] accessed on 15 October 2022.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Augmentation** & **Model** & **Data Type** & \(t\)**-Test (\(p\)-Value)** \\ \hline Proposed vs. Standard & Faster-RCNN & Training & 4.8 \(\times\)\(10^{-3}\) \\ Proposed vs. Standard & Faster RCNN & Validation & 9.1 \(\times\)\(10^{-4}\) \\ Proposed vs. Standard & SSD & Training & 3.6 \(\times\)\(10^{-6}\) \\ Proposed vs. Standard & SSD & Validation & 8.3 \(\times\)\(10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 5: Pairwise comparison between the minimum training and validation losses from the proposed augmentation pipeline and the standard augmentation method.