DNAdithya / nitrate_detection

🧪 Nitrite Color Identification System

A Streamlit-based web application for automated detection and quantification of nitrite concentration in water samples using computer vision and AI-powered color analysis.

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🚀 Features

• AI-Powered Analysis: Uses YOLOv8 for object detection (test strips, tubes, color charts).
• Color Extraction: K-means clustering for dominant color detection in test regions.
• Color Matching: LAB color space comparison to reference color chart for accurate nitrite estimation.
• Confidence Scoring: Each result includes a reliability/confidence score.
• Multi-format Support: Accepts JPG, JPEG, PNG, BMP, TIFF images.
• History Tracking: Stores all test results and images in a local SQLite database.
• Interactive Visualization: Shows detections, color analysis, and recommendations.
• Unit Selection: Supports mg/L and ppm units.
• Downloadable Results: Download analyzed images and review previous tests.

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🖥️ How It Works

1. Upload an Image: Provide a photo of your nitrite test strip or water sample.
2. Object Detection: The app locates relevant regions (test tube, strip, color chart) using YOLO.
3. Color Analysis: Extracts the dominant color from the detected region.
4. Color Matching: Compares the detected color to a reference chart using LAB color space.
5. Result Output: Displays estimated nitrite concentration, confidence, and health recommendations.
6. History: All results and images are saved for future review.

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📦 Installation

1. Clone the repository:

   git clone https://github.com/Dnadithya/nitrate_detection.git
   cd nitrate_detection

2. Install dependencies:

   pip install -r requirements.txt

3. Download YOLOv8 weights:

   • Place your YOLOv8 weights file (e.g., best.pt) in the weights/ directory.
   • If not present, the default YOLOv8n weights will be used.

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🏃‍♂️ Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Open your browser:
   Visit http://localhost:8501 to access the app.

3. Upload and Analyze:

   • Go to "Upload & Test"
   • Upload your image and select options
   • View results, color analysis, and recommendations

4. Review History:

   • Go to "History" to see previous tests and download images

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📝 Reference Levels

Nitrite (mg/L)	Interpretation	Recommendation
0.0	Safe	No action needed
0.5 - 1.0	Low	Monitor regularly
1.0 - 2.0	Moderate	Take action to reduce levels
>2.0	High	Immediate attention required

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Expected Result

🛠️ Technical Details

• Framework: Streamlit
• AI Model: YOLOv8 (Ultralytics)
• Color Analysis: LAB color space, K-means clustering
• Database: SQLite (local file: nitrite.db)
• Image Processing: OpenCV, PIL, scikit-learn

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