A hybrid deep learning and fuzzy logic framework for robust tomato disease detection and classification

Key Points

• Results indicate a high accuracy rate in disease detection using deep learning techniques, and image processing plays a crucial role.
• Key evidence shows over 90% accuracy achieved in classifying various tomato diseases from images—underlining the method's effectiveness.
• Analysis utilizing a hybrid deep learning and fuzzy logic framework enables accurate disease classification.
• This work highlights the potential for improved agricultural practices through advanced detection methods, with broader applications possible.