🌱 Smart Agriculture System

A full-stack Smart Agriculture platform integrating FastAPI, PostgreSQL, AI (Machine Learning), and Streamlit Dashboard. This project is production-ready, deployable via Docker, and GitHub-ready for developers, startups, and research purposes.

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Features

• JWT Authentication – Secure login & role-based access
• Sensor Data Management – CRUD operations for temperature, humidity, soil moisture
• AI Crop Recommendation – ML model predicts the best crop based on environmental conditions
• Streamlit Dashboard – Interactive UI for data visualization and AI recommendations
• Docker Deployment – One-command deployment for backend + database

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Technology Stack

• Backend: FastAPI, Python 3.11
• Database: PostgreSQL
• Machine Learning: scikit-learn, pandas, numpy
• Frontend / Dashboard: Streamlit
• Deployment: Docker & Docker Compose
• Authentication: JWT + passlib bcrypt

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Project Structure

smart-agriculture-system/
│── backend/          # FastAPI backend
│   ├── main.py
│   ├── config.py
│   ├── database.py
│   ├── models.py
│   ├── schemas.py
│   ├── auth.py
│   └── routes/
│       ├── auth_routes.py
│       ├── sensor_routes.py
│       └── ai_routes.py
│
├── ai_models/        # AI datasets & models
│   ├── dataset.csv
│   ├── train_model.py
│   └── crop_model.pkl
│
├── dashboard/        # Streamlit dashboard
│   └── app.py
│
├── requirements.txt
├── Dockerfile
├── docker-compose.yml
├── README.md
└── .gitignore

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Installation & Setup

1. Clone the Repository

git clone https://github.com/bundlab/smart-agriculture-system.git
cd smart-agriculture-system

2. Create Virtual Environment

python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Database

Set up PostgreSQL:

CREATE DATABASE smart_agriculture;
CREATE USER smart_user WITH PASSWORD 'smartpass';
GRANT ALL PRIVILEGES ON DATABASE smart_agriculture TO smart_user;

Update backend/config.py if necessary.

5. Run Backend

uvicorn backend.main:app --reload

Visit: http://127.0.0.1:8000/docs

6. Run Dashboard

streamlit run dashboard/app.py

7. Docker Deployment (Optional)

docker-compose up --build

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Usage

1. Register / Login via /auth endpoints
2. Send Sensor Data via /sensors endpoints
3. Get AI Crop Recommendation via /ai/recommend
4. View data and AI suggestions in Streamlit Dashboard

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Authors

Abdullahi Bundi – GitHub Profile

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License

This project is licensed under the MIT License.

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Future Enhancements

• Real-time IoT sensor integration (ESP32/Arduino)
• Mobile app interface (Flutter or Kivy)
• Advanced AI for disease and pest detection
• Cloud deployment (AWS, Azure, Railway)