Smart Plant Assistant

A full-stack web application built with Flask (backend) and React (frontend) that monitors plant sensor data and uses machine learning to predict optimal watering times based on weather conditions.

Features

• User Authentication: Secure login and registration with session-based authentication
• Plant Management: Add, select, and manage multiple plants with unique sensor IDs
• Real-time Sensor Monitoring: Displays Light, Soil Moisture, and Temperature data
• National Weather Service Integration: Fetches current weather data from the NWS API
• ML-Powered Predictions: Uses a trained model to predict when to water your plant based on:
  • Current soil moisture
  • Environmental conditions (temperature, light)
  • Weather forecast (humidity, precipitation, wind speed)
  • Evapotranspiration calculations
• Plant Health Score: Calculated health score based on sensor data with detailed breakdown
• AI Chatbot: OpenAI-powered chatbot for plant care advice (requires API key)
• Data Visualization: Interactive charts showing sensor data trends and watering predictions
• Location Settings: Set your location for accurate weather data
• Modern UI: Beautiful, responsive React interface with white theme

Project Structure

SmartPlantAssistant/
├── backend/           # Flask API server
│   ├── app.py        # Main Flask application
│   └── requirements.txt
├── frontend/         # React frontend
│   ├── src/
│   │   ├── components/   # React components
│   │   ├── services/     # API service layer
│   │   ├── App.js
│   │   └── index.js
│   └── package.json
└── README.md

Setup Instructions

Prerequisites

• Python 3.8+ (backend)
• Node.js 14+ and npm (frontend)
• Neon Postgres database (free tier available at neon.tech)
• OpenAI API key (optional, for chatbot functionality)

Environment Variables

Create a .env file in the project root with the following:

# Neon Postgres Database URL (required)
DATABASE_URL=postgresql://username:password@ep-xxxxx.us-east-2.aws.neon.tech/neondb?sslmode=require

# Flask Secret Key (required for sessions)
SECRET_KEY=your-secret-key-here

# NWS (National Weather Service) API User-Agent (required for weather)
# Format: AppName-your.email@domain.com
NWS_USER_AGENT=SmartPlantAssistant-your.email@example.com

# OpenAI API Key (optional, for chatbot functionality)
OPENAI_API_KEY=your_openai_api_key_here

Important: Never commit your .env file to version control. It's already in .gitignore.

Backend Setup (Flask)

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Initialize the database (first time only):

   python3 -c "from app import app, db; app.app_context().push(); db.create_all(); print('✅ Database tables created')"

5. Run the Flask server:

   python app.py

   The backend will run on http://localhost:5001 (port 5000 is often used by macOS AirPlay Receiver)

Frontend Setup (React)

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the React development server:

   npm start

   The frontend will run on http://localhost:3001 and automatically proxy API requests to the backend.

Quick Start (Both Services)

Use the provided start script:

./start.sh

This will start both the backend and frontend servers. To stop them:

./stop.sh

API Endpoints

Authentication

• POST /api/register - Register a new user
• POST /api/login - Login user
• POST /api/logout - Logout user
• GET /api/user - Get current user information

Sensor Data

• GET /api/sensor-data - Get current sensor readings for selected plant
• POST /api/sensor-data - Update sensor data (for actual sensor integration)

Weather

• GET /api/weather - Get weather data from NWS API (uses user's saved location)
• PUT /api/user/location - Update user's location

Plants

• GET /api/plants - Get all user's plants
• POST /api/plants - Create a new plant
• PUT /api/plants/<id> - Update a plant
• DELETE /api/plants/<id> - Delete a plant

Predictions & Health

• POST /api/predict - Get watering prediction based on sensor and weather data
• GET /api/plant-health/<plant_id> - Get plant health score

Chatbot

• POST /api/chat - Send message to AI chatbot (requires OpenAI API key)

Raspberry Pi Sensor Integration

This project includes scripts for connecting Raspberry Pi sensors (AHT20, BH1750, Arduino I2C soil moisture) directly to the Neon database.

Quick Setup

1. SSH into your Raspberry Pi and navigate to the project directory

2. Install sensor dependencies:

   pip3 install --user --break-system-packages psycopg2-binary python-dotenv adafruit-blinka adafruit-circuitpython-ahtx0 adafruit-circuitpython-bh1750 RPi.GPIO

3. Set up environment variables (create ~/.env or use systemd service):

   DATABASE_URL=postgresql://username:password@ep-xxxxx.us-east-2.aws.neon.tech/neondb?sslmode=require
   PLANT_ID=1

4. Set up automatic readings (every 10 seconds):

   cd raspberry_pi
   chmod +x setup_10_second_readings.sh
   sudo ./setup_10_second_readings.sh

5. Check service status:

   sudo systemctl status smart-plant-sensor.service

See raspberry_pi/README.md for detailed setup instructions and troubleshooting.

Technologies

• Backend: Flask, Python, NumPy, SQLAlchemy, Flask-Login
• Frontend: React, Chart.js, Axios
• ML: Custom neural network model for predictions
• APIs: National Weather Service API, OpenAI API (optional)
• Database: Neon Postgres (cloud-hosted, accessible from Raspberry Pi and backend)
• Authentication: Session-based with Flask-Login

Browser Compatibility

Works in all modern browsers that support:

• ES6 JavaScript
• Fetch API
• Geolocation API
• Canvas API (for charts)

Development Notes

• The backend uses CORS to allow React frontend to communicate
• Sensor data comes from Raspberry Pi sensors connected to Neon Postgres database
• Weather data is fetched from NWS API based on user's saved location (must be set in Location Settings)
• Predictions update every 5 seconds along with sensor data
• All data displayed is real-time from physical sensors (no simulated data)

Security Notes

• Never commit your .env file to version control
• The .env.example file shows the required environment variables without sensitive data
• Database files (.db) are excluded from version control
• Generate a strong SECRET_KEY for production deployments

License

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Contributing

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