Automated Marine Animal Identification and Detection of Species

Mission: Empower fishermen through cutting-edge technology by revolutionizing the fishing industry with a sustainable and efficient approach.

Project Overview

The INCOIS_AAIDeS Project leverages advanced deep learning techniques to analyze fish behavior directly from the net. This innovative system aims to:

• Optimize fishing efficiency.

• Promote sustainability in marine ecosystems.

• Provide actionable insights to fishermen for better decision-making.

  

Features

• Deep Learning Model: Analyzes fish species and behaviors using real-time data.
• Automation: Reduces manual labor and errors in fish identification.
• Sustainability: Ensures responsible fishing practices by monitoring fish populations.

File Structure

fish.py

This is the core script for the AAIDeS project. It includes:

• Model Implementation: Deep learning algorithms for detecting and identifying fish species.
• Data Processing: Functions for preprocessing inputs (images/videos).
• Prediction Pipeline: Generates insights based on netted fish behavior.

Prerequisites

• Programming Language: Python 3.7+
• Libraries:
  • TensorFlow
  • OpenCV
  • NumPy
  • Matplotlib
  • Pandas

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/INCOIS_AAIDeS.git

2. Navigate to the project directory:

   cd INCOIS_AAIDeS

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Run the script to process sample data:

   python fish.py

2. Upload fish images or videos to analyze their behavior and species.

Contributing

We welcome contributions to enhance the AAIDeS project. Please follow these steps:

1. Fork the repository.
2. Create a new branch (feature/your-feature-name).
3. Commit your changes.
4. Submit a pull request.

License

This project is licensed under the MIT License.