🐔 Chicken Disease Classification – End-to-End Project

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📌 Project Overview

Poultry farming is one of the most important agricultural practices worldwide, providing protein and livelihood for millions of people. However, poultry diseases like Coccidiosis pose a major threat to healthy chicken production.

Coccidiosis is a parasitic disease caused by protozoa of the genus Eimeria. It affects the intestinal tract of chickens, leading to symptoms such as diarrhea, weight loss, reduced egg production, and even death in severe cases. Early detection is crucial to prevent economic loss and improve food security.

This project provides an AI-powered solution using Deep Learning (VGG16 pre-trained model) to automatically classify chicken images into:

• ✅ Healthy
• ❌ Coccidiosis Infected

By integrating CI/CD pipelines, GitHub Actions, DVC (Data Version Control), and a Flask Web App, this project demonstrates a full-stack MLOps workflow for real-world deployment.

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🚀 Features

• 📂 Data Ingestion

• 🧠 Model Building – VGG16 pre-trained model fine-tuned for classification

• 🔄 Training Pipeline – Includes callbacks, checkpoints, and logging

• 📊 Evaluation – Reports model performance with metrics

  Example Output:

  {
      "loss": 1.2301383018493652,
      "accuracy": 0.9568965435028076
  }
  
  

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⚙️ Key Features

• ⚙️ Custom Exception Handling – With BoxValueError for robust error tracking
• 📝 Logger – Centralized logging for all project components
• 🔗 CI/CD – Continuous integration & deployment using GitHub Actions
• 📦 DVC – Data & model version control for reproducibility
• 🌐 Web App – Flask application with HTML, CSS, and JavaScript (Home, Form, Result pages placeholders included)

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📁 Project Structure

Chicken-Disease-Classification-End-to-End-Project
│── .dvc/ # DVC metadata
│── artifacts/ # Stored models, metrics, etc.
│── config/ # YAML configuration files
│── datasets/ # Raw and processed datasets
│── logs/ # Training & pipeline logs
│── research/ # Experiment notebooks
│── src/ # Source code
│ ├── components/ # Data ingestion, training, evaluation, etc.
│ ├── config/
│ ├── constants/
│ ├── entity/
│ ├── logging/
│ ├── pipeline/
│ ├── utils/
│ └── init.py
│── static/ # CSS, JS files for web app
│── templates/ # HTML templates (home.html, form.html, result.html)
│── app.py # Flask application entry point
│── .github/ # GitHub workflows (CI/CD)
│── .gitignore
│── .dvcignore
│── README.md

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🖼️ Screenshots

Home – Project introduction

Form - Upload chicken image for prediction

Result - Displays prediction result

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⚙️ Tech Stack

• Deep Learning: TensorFlow / Keras (VGG16 Pre-trained Model)
• MLOps Tools: DVC, GitHub Actions, Logging, Custom Exception
• Web Framework: Flask (HTML, CSS, JavaScript frontend)
• Version Control: Git, GitHub
• Deployment Ready: CI/CD pipeline

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📊 Model Performance

Metric	Value
Loss	1.2301
Accuracy	95.69%

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🖥️ How to Run

1. Clone the repository

   git clone https://github.com/AdMub/Chicken-Disease-Classification-End-to-End-Project.git
   cd Chicken-Disease-Classification-End-to-End-Project

2. Create and activate a virtual environment

python -m venv venv
source venv/bin/activate    # Linux/Mac
venv\Scripts\activate       # Windows

3. Install dependencies

pip install -r requirements.txt

4. Run DVC pipeline

dvc repro

5. Start the Flask app

python app.py

6. Open in browser

http://127.0.0.1:8080

🐳 Docker Setup

Build and run backend in Docker:

docker build -t chicken-disease-classifier .
docker run -p 8080:8080 chicken-disease-classifier

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⚡ CI/CD (GitHub Actions)

This project uses GitHub Actions for automation:

• ✅ Code linting & testing
• 🐳 Build & push Docker image
• ☁️ Deploy to AWS

Workflow file: .github/workflows/main.yml

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📜 License

This project is licensed under the MIT License – see the LICENSE file for details.

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✨ Acknowledgments

• TensorFlow & Keras for deep learning framework

• DVC for experiment tracking and reproducibility

• GitHub Actions for CI/CD automation

• Poultry farmers & veterinary experts for inspiring this solution

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👨‍💻 Author

Mubarak Adisa

• 🎓 Civil Engineering + Computer Science (Data Science & AI Focus)
• 🔗 GitHub: AdMub
• 💼 LinkedIn: Mubarak Adisa