The project was developed as part of the B.Tech in Artificial Intelligence program, combining deep learning techniques with image processing to create a practical solution for real-world agricultural challenges. The trained model can classify multiple crop diseases with high accuracy and provide instant predictions through a simple user interface.

Objectives

• Detect common crop diseases using leaf images.
• Reduce the need for manual inspection.
• Support farmers with faster disease identification.
• Demonstrate the practical application of AI in agriculture.

The developed model achieved over 94% classification accuracy on the testing dataset and successfully demonstrated how AI can assist modern agriculture by providing quick, reliable disease detection. The project highlights the potential of intelligent technologies to improve productivity while making advanced tools more accessible to farmers.