Cassava Disease Detection

About the Project

This research project developed a computer vision model to identify diseases in cassava crops — a staple food for over 500 million people in Africa. Using image classification techniques, the model can distinguish between healthy plants and four common cassava diseases from leaf photographs, achieving 92% accuracy in field tests. The goal was to give smallholder farmers a practical tool for early disease detection.

Key Highlights

• Achieved 92% classification accuracy across 5 categories (healthy + 4 disease types)
• Trained on thousands of field-captured leaf images with data augmentation techniques
• Built with TensorFlow and OpenCV for image preprocessing and model inference
• Implemented transfer learning from pre-trained models to work with limited training data
• Designed for deployment on mobile devices for use in rural farming communities