Tomato Disease

Tomato disease detection is a crucial area of research aiming to improve crop yields and reduce economic losses through early and accurate diagnosis. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and Siamese networks, often incorporating techniques like tensor subspace learning to enhance classification accuracy and efficiency, even on limited or imbalanced datasets. These advancements are leading to the development of user-friendly mobile applications that provide farmers with real-time disease identification and remedy suggestions, bridging the gap between advanced technology and practical agricultural applications. The ultimate goal is to create robust and accessible tools for effective disease management, improving food security and sustainability.