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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), tungro (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 Fréchet 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 9.1×10−4 and 8.3×10−5. 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.

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