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Generative_AI_with_poultry_disease_detection_system_v2 / README.md
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title: Generative AI With Poultry Disease Detection System V2
emoji: 🐨
colorFrom: pink
colorTo: gray
sdk: docker
pinned: false
license: mit

Here’s the updated README.md with an improved structure and updated to-do list:

🐔 Poultry Farming Assistance and Management System

This project combines a Poultry Farming Assistance System and a Poultry Management System, streamlining health monitoring, task management, inventory tracking, and disease detection using AI. Designed for farmers and farm admins, this system enables effective poultry health management, provides real-time alerts, and generates actionable insights to enhance productivity.

Key Features

  1. User Authentication and Role-Based Access: Secure registration and login for farmers and admins.
  2. Health Management with AI-Driven Disease Detection: Fecal image analysis for common poultry diseases and personalized treatment recommendations.
  3. To-Do List and Task Management: Track daily farm tasks and share across groups.
  4. Inventory Management: Monitor feed, medication, and supply levels with automated alerts.
  5. Data Logging and Reporting: Real-time dashboards, export options, and health reports.
  6. Notification System: Email alerts for tasks, health conditions, and inventory updates.

System Overview

Poultry Farming Assistance System

  • AI-Powered Disease Detection: Uses pre-trained models (e.g., Final_Chicken_disease_model.h5) to analyze fecal images and identify poultry diseases.
  • Health Monitoring: Tracks metrics such as weight loss, mortality rate, and feed intake for proactive care.
  • Treatment Recommendations: Offers tailored suggestions based on detected health issues.

Poultry Management System

  • Task and To-Do List Management: Allows farm admins to assign tasks and track completion across groups.
  • Inventory Tracking: Manages inventory levels and provides alerts for low-stock items.
  • Health Records: Logs poultry health data and tracks disease outbreaks.
  • Reporting: Generates farm performance reports and health metrics.

Use Cases

  • Farmers: Access disease detection, complete tasks, view health records, and receive alerts.
  • Admins: Create tasks, monitor group activities, manage inventory, and ensure flock health.

📝 To-Do List

1. Setup and Configuration

  • Set up a virtual environment and install required packages (FastAPI, TensorFlow, MongoDB, transformers, etc.).
  • Configure MongoDB and test CRUD operations.
  • Establish environment variables for MongoDB, email credentials, and Hugging Face API token.

2. Authentication System

  • Develop user registration for farmers and admins.
  • Implement login/logout with JWT.
  • Set up password encryption (bcrypt) for secure storage.

3. Poultry Farming Assistance System

  • Integrate the poultry disease detection model (Final_Chicken_disease_model.h5).
  • Set up image preprocessing for disease detection.
  • Build endpoints for image upload and disease analysis.
  • Generate health-related notifications and recommendations.
  • Implement real-time health monitoring and alerts.

4. To-Do List Management

  • Design MongoDB schema for to-do lists.
  • Create routes for farmers to view and mark to-do items.
  • Build functionality for admins to assign tasks.
  • Develop an AdminLTE 4-based UI for managing tasks.

5. Data Logging and Reporting

  • Implement data logging for activities and health records.
  • Build reporting dashboards using AdminLTE.
  • Enable export functionality (PDF, XLS).
  • Integrate real-time visualization tools for health and productivity insights.

6. Health Management

  • Define MongoDB schema for health records.
  • Integrate disease detection data into health management.
  • Develop an AdminLTE dashboard for disease and health tracking.
  • Automate health alerts and treatment recommendations.

7. Inventory Management

  • Design MongoDB schema for inventory tracking.
  • Implement routes for adding, updating, and managing inventory items.
  • Create an AdminLTE dashboard for inventory status.
  • Set up email alerts for low-stock items.

8. Notification System

  • Configure email service for notifications (smtplib or nodemailer).
  • Implement notifications for:
    • Task completion.
    • Health alerts and recommendations.
    • Inventory updates and low stock alerts.
  • Test the notification system across scenarios.

9. Group Management

  • Create MongoDB schema for managing user groups.
  • Allow admins to create groups and share tasks.
  • Enable farmers to join groups and track shared tasks.

10. Testing and Debugging

  • Write unit tests for modules (authentication, to-do lists, health management, inventory, etc.).
  • Conduct integration testing to validate end-to-end functionality.
  • Debug MongoDB transactions, pytorch predictions, and AdminLTE dashboards.

11. Deployment

  • Deploy the application on Hugging Face Spaces or other cloud platforms.
  • Configure production settings (environment variables, security).
  • Set up CI/CD pipeline for automatic testing and deployment.

Future Enhancements

  • Mobile Application: Develop a mobile app using Flutter for remote management.
  • AI-Based Inventory Forecasting: Predict inventory needs based on usage trends.
  • Multi-Language Support: Localize the interface for global users.
  • Extended Disease Detection: Add models for additional poultry diseases to improve health management.