AI-Powered Maize Leaf Disease Detection
ZeaWatch is a mobile application designed to help farmers, researchers, and agricultural extension officers detect maize leaf diseases using artificial intelligence. By simply capturing or uploading a photo of a maize leaf, ZeaWatch identifies potential diseases and suggests solutions to improve crop health and yield.
- 📸 Image Capture & Upload – Take a photo or upload an existing maize leaf image.
- 🤖 AI-Powered Detection – Uses a hybrid CNN + Transformer model for high-accuracy disease classification.
- 💡 Actionable Insights – Provides recommended solutions and management practices.
- 🌍 Offline Functionality (Planned) – Designed to work in rural areas with limited internet.
- 🔒 User-Friendly & Secure – Simple interface with data privacy considerations.
- Farmers seeking early detection of crop diseases.
- Agricultural extension officers.
- Researchers and students in agriculture and AI.
- NGOs and government bodies working in food security.
- Frontend: Flutter (Dart)
- Backend: FastAPI (Python)
- Machine Learning: TensorFlow / PyTorch (CNN + Transformer hybrid model)
- Database: PostgreSQL (for storing disease insights and user feedback)
- Deployment: Docker + Cloud (AWS/GCP/Azure)
- Maize Leaf Blight
- Maize Streak Virus
- Gray Leaf Spot
- Healthy Leaf
(More categories to be added as dataset grows)
Contributions are welcome! Please fork the repo, create a new branch, and submit a pull request.
This project is licensed under the MIT License – see the LICENSE file for details.