An Efficient Transfer Learning Framework for Real-Time Apple Disease Classification Using Deep Learning Models

Puntos clave

• The central aim is to develop a transfer learning framework that enhances the real-time classification of apple diseases using deep learning techniques.
• Utilized deep learning models for classification tasks
• Implemented transfer learning to improve learning efficiency
• Tested the framework on various apple disease datasets
• Achieved high accuracy in real-time classification
• Demonstrated significant improvements over traditional methods
• Showed potential for practical applications in agriculture

Resumen