README.md

metadata

title: Generative AI With Poultry Disease Detection System V2
emoji: 🐨
colorFrom: pink
colorTo: gray
sdk: docker
pinned: false
license: mit

🐔 Poultry Farming Assistance and Management System

This project integrates both a Poultry Farming Assistance System and a Poultry Management System. The system supports farm management tasks like health management, to-do lists, inventory, and notifications, while also providing intelligent assistance for disease detection and recommendations using AI.

Project Features

1. User Registration/Login System
2. Data Logging and Reporting (dashboards and exporting PDF and XLS)
3. Health Management and Disease Detection
4. Inventory Management
5. Poultry Farming Assistance (AI-based disease detection and recommendations)
6. Notification System via Email

System Components

Poultry Farming Assistance System

• AI-based disease detection using machine learning models (e.g., Final_Chicken_disease_model.h5).
• Health monitoring and suggestions for poultry care.
• Fecal image analysis to detect common poultry diseases.
• Generate actionable insights based on poultry health data.

Poultry Management System

• To-Do List Management: Manage daily farm tasks and activities.
• Inventory Management: Track and manage feed, medicines, and other supplies.
• Health Records: Log poultry health issues, treatments, and disease outbreaks.
• Group Management: Farmers can join groups, and admins can create and share to-do lists across groups.
• Reporting: Generate reports on farm performance, health metrics, and more.

Use Cases

• Poultry Farmer: Can sign up, log in, access disease detection tools, complete to-do lists, view health reports, and receive notifications.
• Poultry Farm Admin: Can create and assign tasks, manage inventory, track group activities, and monitor health conditions across the farm.

📋 Development To-Do Checklist

1. Setup and Configuration

• Set up a virtual environment (e.g., venv, conda) for the project.
• Install necessary Python packages: FastAPI, AdminLTE, MongoDB, TensorFlow, Keras, pymongo, transformers, etc.
• Configure MongoDB connection and test basic CRUD operations with MongoDB.
• Set up environment variables for MongoDB URI, email credentials, and other sensitive data.

2. Authentication System

• Build the user registration system (Poultry Farmer, Poultry Admin).
• Implement login and logout functionality using JWT.
• Set up password encryption (e.g., bcrypt) for secure storage.
• Implement session management and token validation.

3. Poultry Farming Assistance System

• Integrate poultry disease detection model (Final_Chicken_disease_model.h5).
• Create an image preprocessing pipeline for disease detection.
• Build routes to upload and analyze poultry fecal images.
• Develop health-related notifications and treatment suggestions.
• Implement real-time health monitoring and alert system for farmers.

4. To-Do List Management

• Create MongoDB schema for to-do lists (todo_list.py).
• Implement routes and controllers for farmers to view and complete to-do lists.
• Implement routes and controllers for admins to create and share to-do lists.
• Design AdminLTE 4-based UI for displaying and managing to-do lists.

5. Data Logging and Reporting

• Develop a data logging system for tracking activities.
• Set up reporting dashboards using AdminLTE (instead of Shiny).
• Implement export functionality for data as PDF and XLS.
• Integrate data visualization tools for real-time reporting and insights.

6. Health Management

• Create MongoDB schema for storing poultry health records (health_record.py).
• Integrate the poultry disease detection model with health management.
• Develop an AdminLTE dashboard for tracking health issues and disease management.
• Implement health-related notifications and treatment suggestions for farmers.

7. Inventory Management

• Build MongoDB schema for inventory management (inventory.py).
• Develop routes for adding, updating, and deleting inventory items.
• Create an AdminLTE dashboard for tracking inventory levels and status.
• Set up alerts for low stock levels via email notifications.

8. Notification System

• Set up an email service for sending notifications to users (e.g., smtplib or nodemailer).
• Implement notification triggers for the following:
  • To-do list completion.
  • Health alerts and treatment recommendations.
  • Inventory updates and low stock alerts.
• Test email notification system for various scenarios.

9. Group Management

• Create MongoDB schema for group management (group.py).
• Allow farm admins to create and delete groups.
• Allow poultry farmers to join groups.
• Implement group sharing functionalities (e.g., sharing to-do lists across groups).

10. Testing and Debugging

• Write unit tests for each module (authentication, to-do lists, health management, inventory, notifications).
• Conduct integration testing to ensure all components work together.
• Debug issues related to MongoDB transactions, AdminLTE dashboards, and TensorFlow model predictions.

11. Deployment

• Deploy the system on Hugging Face Spaces.
• Configure the production environment (e.g., environment variables, security settings).
• Set up a continuous integration/continuous deployment (CI/CD) pipeline for automatic testing and deployment.

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Future Enhancements (Backlog)

• Add mobile app support for poultry farmers using Flutter.
• Implement AI-based inventory prediction for restocking supplies.
• Enable multi-language support for different regions.
• Introduce additional disease models for enhanced health management.

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