  Numerous applications have resulted from the automation of agricultural
disease segmentation using deep learning techniques. However, when applied to
new conditions, these applications frequently face the difficulty of
overfitting, resulting in lower segmentation performance. In the context of
potato farming, where diseases have a large influence on yields, it is critical
for the agricultural economy to quickly and properly identify these diseases.
Traditional data augmentation approaches, such as rotation, flip, and
translation, have limitations and frequently fail to provide strong
generalization results. To address these issues, our research employs a novel
approach termed as PotatoGANs. In this novel data augmentation approach, two
types of Generative Adversarial Networks (GANs) are utilized to generate
synthetic potato disease images from healthy potato images. This approach not
only expands the dataset but also adds variety, which helps to enhance model
generalization. Using the Inception score as a measure, our experiments show
the better quality and realisticness of the images created by PotatoGANs,
emphasizing their capacity to resemble real disease images closely. The
CycleGAN model outperforms the Pix2Pix GAN model in terms of image quality, as
evidenced by its higher IS scores CycleGAN achieves higher Inception scores
(IS) of 1.2001 and 1.0900 for black scurf and common scab, respectively. This
synthetic data can significantly improve the training of large neural networks.
It also reduces data collection costs while enhancing data diversity and
generalization capabilities. Our work improves interpretability by combining
three gradient-based Explainable AI algorithms (GradCAM, GradCAM++, and
ScoreCAM) with three distinct CNN architectures (DenseNet169, Resnet152 V2,
InceptionResNet V2) for potato disease classification.
