Lake Water Quality Estimation System

A deep learning-based system that uses CNN to analyze satellite imagery for estimating lake water quality. The system provides binary classification (good/bad) of water quality to aid environmental monitoring and decision-making.

Features

• CNN-based water quality classification
• User-friendly web interface for image upload
• Visualization of prediction results
• Secure storage of images and analysis results
• Built with MERN stack and Flask

Tech Stack

• Frontend: React.js with Tailwind CSS
• Backend: Node.js, Express.js, Flask
• Database: MongoDB
• ML Framework: CNN (Convolutional Neural Network)
• Deployment: AWS/Heroku

System Requirements

Software Requirements

• Windows/Linux/MacOS
• Node.js
• Python 3.x
• MongoDB

Hardware Requirements

• Processor: Intel i5 or equivalent
• RAM: 8GB minimum
• Storage: 20GB free space
• GPU: Optional but recommended for model training

Installation

1. Clone the repository

git clone https://github.com/Abhinay2206/lake-water-quality

2. Install backend dependencies

cd backend
npm install

3. Install frontend dependencies

cd frontend
npm install

4. Set up python environment

# Create .env file in backend directory
cd cnn-pytorch
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

5. Start the servers

# Start backend server
cd backend
npm start

# Start Flask server
cd cnn-pytorch
python app.py

# Start frontend development server
cd frontend
npm start

Project Structure

├── frontend/               # React frontend
├── backend/               # Node.js & Express backend        
├── cnn-pytorch/           # Trained ML models
└── data/               # Dataset storage

Features and Functionality

Image Upload

• Supports satellite image uploads
• Automatic preprocessing of uploaded images

Model Processing

• CNN-based feature extraction
• Binary classification (good/bad water quality)
• Minimum 75% classification accuracy

Visualization

• Display of prediction results
• Basic result visualization

API Endpoints

• POST /api/upload - Upload satellite images
• GET /api/predict - Get prediction results
• GET /api/history - View prediction history

Security

• Basic authentication system
• Secure image storage
• Limited access controls

Performance

• Processing time: <10 seconds per image
• Accuracy: >75% classification accuracy
• Scalable architecture for future enhancements

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request