Leaf Disease

Leaf disease detection is a critical area of research aiming to improve crop yields and food security through early and accurate diagnosis. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), often incorporating techniques like data augmentation and adversarial training to enhance model robustness and accuracy. These advancements are applied across various crops, leveraging image processing and machine learning to automate disease identification, reducing reliance on manual inspection and potentially minimizing economic losses from crop damage. The integration of these models with explainable AI (XAI) methods is also gaining traction, aiming to increase transparency and trust in automated diagnostic systems.