Potatoes are one of the most highly consumed foods in the world. Due to
several environmental and climate changes, potato leaf diseases are emerging
frequently, causing severe damage to potato plants and drastically reducing potato crop
yields, which results in financial losses to farmers. This research presents a deep
learning generative approach to enhance the accuracy of identifying classical diseases
in potato leaves using a novel data augmentation method, specifically the diffusion
model. By employing diffusion models, researchers created images resembling real
ones showing diseases such as Early Blight, Late Blight, and healthy leaves on potato
leaves. These generated images are then combined with others to strengthen the
training of a deep learning model, enabling it to better distinguish between early blight,
late blight, and healthy leaves. This combination of synthetic data enhances the
model’s robustness. Demonstrates promising advancements in analyzing potato leaf
disease. The significance of this study lies in addressing challenges related to data
availability while improving the model's ability to generalize across the stages of the
disease. The outcomes have implications for agriculture by providing effective diseaseidentification methods for potato farmers. The proposed CNN model delivered
classification accuracy (98.99%) with ResNet18 integrating with the diffusion model
on the potato leaf disease image dataset. Integrating diffusion models represents a step
towards optimizing crop management and maximizing yields in potato cultivation.
Keywords: Artificial neural network, Credibility limits, Loan approval, Loan prediction, Machine learning.
