Java Water Quality Detector

The Challenge

Traditional water quality testing has significant limitations:

• Inconsistent manual observations
• Slow processing times
• Poor pollution tracking capabilities
• Limited scalability

The Solution

I developed an automated system using advanced graph theory and image processing techniques to provide fast, accurate water quality classification.

Key Features

• Smart edge pruning for efficient graph processing
• Adaptive superpixels for intelligent image segmentation
• Real-time progress tracking with accurate progress bar
• Robin Hood probing for optimized hash table performance
• Alpha-based similarity detection for contamination trends
• Multiple contamination levels (undrinkable, contaminated, dirty)

Technical Highlights

• Implemented complex graph algorithms (MST, Graph Edit Distance)
• Designed scalable architecture for image processing pipeline
• Optimized performance for real-time detection
• JavaFX-based intuitive user interface
• Custom data structures for memory efficiency

What I Learned

This project pushed me to implement advanced algorithms I’d only seen in textbooks. I learned about:

• Graph theory practical applications
• Image processing optimization techniques
• Memory-efficient data structure design
• UI/UX for scientific applications

🔗 Dive Deeper

Want to explore the code, API endpoints, and full technical documentation? View the complete README on GitHub ➔