pen.live: Blockchain-Powered Livestock Marketplace with Real-Time Pen Analysis

pen.live merges livestock ownership & management with the power of blockchain technology. It creates a secure and transparent tokenized marketplace for animals, where a unique NFT represents each animal. This NFT grants ownership and provides detailed tracking and knowledge of the individual animal through Precision Livestock Farming (PLF) technology. Precision Livestock Farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time.

Guardian Network and Revenue Sharing

Guardians, local to the farms, play a crucial role. They onboard farmers and stake an amount equal to the initial cost of the animal's token. Should an animal die or be lost, a moderator/inspector team determines the cause and liability. Based on their vote, the NFT holder may be compensated by slashing the guardian's stake. NFT holders pay for animal management, with revenue split transparently between the farmer, guardian, and pen.live.

Comprehensive Platform with Live Pen Analysis

You can track listed pens, total pens, platform value locked (TVL), total listed animals, trending animal TVL per pen, and top holders with their total realized and unrealized profit or loss (PnL). Additionally, you can see dedicated sections for farmer, NFT holder, guardian reports, revenues, and a live stream with PLF features.

Live Pen Analysis for Real-Time Insights & Reports

The Live Pen Analysis Live Stream Web Application empowers farmers and investors(NFT holders) to gain real-time insights into their pens and livestock. Users can process live video streams and perform real-time pen analysis using the YOLO (You Only Look Once) model. This application utilizes Ultralytics YOLO for object tracking and Pytorch for analysis. A user-friendly interface allows for control over the video stream, including flipping the video horizontally and running object detection. Metrics include: Animal growth rate(weight/height), feeding habits, disease detection, pregnancy + offspring linking etc

Secure Wallet Integration and Streamlined Fee System

Upon registration by local Guardian, farmers can connect their wallets to mint individual animal NFTs, and connects with potential buyers through the pen.live platform. We're intergrating offramp of Revenue for farmers, investors and guardinans.

This unique combination of secure blockchain ownership, real-time animal tracking, and advanced pen analysis empowers farmers with scientific livestock management while offering investors a new and exciting way to participate in the livestock market.

Features

• Fetch live video streams or videos from URLs using Streamlink.
• Perform real-time object detection using the Ultralytics YOLO model.
• Allow users to toggle preview, flip the video horizontally, and run object detection.
• Adjust object detection confidence threshold using a slider.
• Display real-time object detection results on the live stream.
• Livestock Reports & Tracking listed pens, total pens, platform value locked (TVL), total listed animals, and trending animal TVL per pen.
• View top holders with their total realized and unrealized profit or loss (PnL).
• Access dedicated sections for farmer and guardian revenue, platform revenue, and a live stream with advanced features.
• Integrate seamlessly with a secure farmer's wallet system for pen monitoring and potential buyer connections.
• Facilitate a streamlined fee system deducted from the farmer's wallet for real-time pen analysis.

Screenshots

Dashboard	Analysis

Prerequisites

Before running the web application, ensure you have the following prerequisites installed on your system:

• Python 3.10
• pip (Python package manager)

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/Clinton-Nyaore/Web3Clubs-Goats-CV.git
   

2. Navigate to the project directory:

3. Install the required Python packages using pip:

   pip install -r requirements.txt
   

Usage

1. Run the Flask application:

   python app.py
   

2. Open your web browser and go to http://localhost:5000 to access the application's homepage.

3. On the homepage, enter the URL of the video/live stream you want to process.

4. Click on the "Start Stream" button to initiate the video stream processing.

5. The video stream with real-time object detection will be displayed on the index page.

6. Use the control features (checkboxes and slider) to modify the behaviour of the video stream and object detection.

7. To stop the video stream processing, click the "Back to Homepage" button.

How it Works

The Live Object Detection web application is built using the Flask framework and utilizes OpenCV for video stream processing. The YOLOv8 model is employed for real-time object detection.

1. When the user enters the video/live stream URL and clicks "Start Stream," the VideoStreaming class initiates the video stream processing.

2. The video stream is obtained from the specified URL using the cv2.VideoCapture function from OpenCV.

3. The user can control various settings, such as previewing the stream, flipping the video horizontally, and enabling object detection.

4. When object detection is enabled, the YOLOv8 model predicts the objects in each video frame with a specified confidence threshold.

5. The detected objects and their confidence scores are displayed in real-time on the web page using Socket.IO for dynamic updates.

Control Features

The application provides the following control features:

• Show Stream: This checkbox allows users to toggle the preview of the video stream. When checked, the stream is visible; otherwise, a placeholder image is displayed.

• Flip Horizontally: This checkbox allows users to flip the video stream horizontally. When checked, the video will be horizontally mirrored.

• Run Detection: This checkbox enables or disables real-time object detection. When checked, the YOLOv8 model performs object detection on each frame.

• Confidence Threshold: Users can adjust the confidence threshold for object detection using the slider. The confidence threshold determines the minimum confidence required for an object to be detected.

Technologies Used

• Python 3
• Solidity
• Flask (Web Framework)
• OpenCV (cv2) (Video Stream Processing)
• YOLOv8 (You Only Look Once) Model for Object Detection
• Socket.IO (For Real-Time Updates)
• Bootstrap (Frontend Styling)
• HTML/CSS/JavaScript
• PyTorch

Supported Video Platforms

This application supports video streams from a variety of platforms, including but not limited to:

1. Twitch
2. YouTube
3. Dailymotion
4. Facebook
5. Mixer
6. Periscope
7. Vimeo
8. Livestream
9. Steam Broadcasting
10. and more...

Please refer to the official Streamlink documentation for an up-to-date list of supported platforms: Streamlink Documentation

Next Steps

• Advanced Analytics:
  • Implement additional functionalities for pen analysis, such as:
    • Animal health detection (e.g., lameness, disease)
    • Behavior analysis (e.g., stress levels, feeding patterns)
    • Breed recognition and count
• AI Model Improvement:
  • Train the YOLO model with a larger dataset for improved accuracy in animal detection and tracking.
  • Explore other deep learning models potentially suited for specific pen analysis tasks.
• Integration with Smart Farming Systems:
  • Allow data export to integrate with existing farm management software.
  • Enable control of smart farm devices (e.g., automated feeders) based on pen analysis results.
• Enhanced Security:
  • Implement multi-factor authentication for secure access to farmer wallets.
  • Explore blockchain technology for secure data storage and transaction verification.
• Mobile App Development:
  • Develop a mobile application for farmers to access pen monitoring functionalities on the go.
• Buyer Matching:
  • Integrate an algorithm to match farmers with potential buyers based on pen analysis data and buyer preferences.
• Subscription Model:
  • Explore a tiered subscription model offering different levels of service (e.g., basic monitoring vs. advanced analytics) at varying price points.

Acknowledgments

• Ultralytics YOLO for providing the object detection model.
• Streamlink for video processing from various platforms.
• Flask, Bootstrap, jQuery, and SocketIO for the web application framework.