Shrimp Seed Detection and Counting

Overview

This project implements an automated system for detecting and counting shrimp seeds/larvae using computer vision techniques. Built as a Streamlit-based web application, it provides shrimp farmers and hatchery operators with an easy-to-use tool for real-time counting, eliminating the need for manual processes. The system is designed to improve population density monitoring, inventory management, and feeding optimization.

Vision Behind the Project

The inspiration for this project stems from Bhimavaram, a village in Andhra Pradesh, India, where aquaculture is a primary source of livelihood. Farmers in this region often rely on manual counting of shrimp seeds before purchase, which is both time-consuming and prone to errors. This project was initiated with the vision of creating a scalable solution that could eventually evolve into a mobile application, making it even more accessible to farmers in rural areas. While this version is implemented as a web-based tool, it lays the groundwork for future enhancements that align with this vision.

Features

• Automated Detection: Identifies individual shrimp seeds in images or video streams using advanced computer vision models.

• Accurate Counting: Provides precise counts with minimal margin of error (93-96% accuracy).

• Batch Processing: Processes multiple images simultaneously for large-scale analysis.

• User-Friendly Web Interface: Built using Streamlit, offering an intuitive platform for image upload and result visualization.

• Report Generation: Exports detailed reports with count statistics and visualizations.

• Scalable Design: Can be extended to support mobile applications or cloud-based solutions in the future.

Technology Stack

• Programming Language: Python 3.11
• Computer Vision: OpenCV, TensorFlow/PyTorch, detectron2 (from facebook)
• Image Processing: NumPy, SciPy
• Model Architecture: YOLOv5/Faster R-CNN for object detection
• UI Framework: Streamlit/Flask (web interface)

Installation

Prerequisites

• Python 3.11 or higher
• CUDA-compatible GPU (recommended for faster processing)
• Webcam or digital camera for live counting

Setup

# Clone the repository
git clone https://github.com/rishendra-manne/shrimp_seeds_detection.git
cd shrimp_seeds_detection

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

### Web Interface
```bash
# Start the web application
python app.py

Then open your browser and navigate to http://localhost:8501

Special installation note for detectron2: Since detectron2 is not available directly via pip, it should be installed separately with:

# For CUDA-enabled GPU:
pip install 'gitthttps: //github.com/facebookresearch/detectron2.git'
# For CPU-only:
pip install 'git+https://github.com/facebookresearch/detectron2.git@main#subdirectory=projects/PointRend'

Model Training

If you need to train the model on your specific prawn species or environment:

# Train the detection model with custom data
python src/pipelines/training_pipeline.py --data_path ./data/custom_dataset --epochs 50 --batch_size 16 --model yolov5_custom.pt

Performance Metrics

• Counting Accuracy: 93-96% under optimal conditions
• Processing Speed: 2-3 seconds for a image
• Minimum Detectable Size: 0.5mm larvae
• Optimal Water Clarity: 80%+ transparency of thin layer

Troubleshooting

Common Issues

1. Poor Detection Accuracy

   • Ensure proper lighting conditions
   • Check water clarity and background contrast
   • Adjust detection threshold in config.yml

2. Slow Processing

   • Reduce input image resolution
   • Enable GPU acceleration
   • Use batch processing for large datasets

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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