Crop Health Management System This project is designed to monitor crop health using real-time camera feeds, machine learning-based disease detection, and IoT sensors. It helps detect plant diseases and update inventory automatically.
Features Real-time plant disease detection via ESP32-CAM
Machine learning model for disease classification
Inventory update system for tracking diseased plants
Flask-based backend server for data processing
SQLite database for storing inventory data
Tech Stack Machine Learning: TensorFlow, Keras
IoT: ESP32-CAM
Backend: Flask
Database: SQLite
Computer Vision: OpenCV
Getting Started
- Clone the Repository bash Copy Edit git clone https://github.com/your-username/crop-health-management.git cd crop-health-management
- Install Requirements bash Copy Edit pip install -r requirements.txt
- Run Flask Server bash Copy Edit cd server python app.py
- Upload Image or Stream from ESP32-CAM Ensure that the ESP32-CAM is sending images to the Flask server.
Project Structure
crop-health-management/ │ ├── model/ # ML model and preprocessing code ├── server/ # Flask server and database integration ├── esp32/ # ESP32-CAM firmware code ├── utils/ # Helper scripts └── README.md # Project documentation
Future Scope Integrate weather and soil data
Deploy on Raspberry Pi for edge inference
Add mobile app support for real-time alerts
License This project is licensed under the MIT License.