.

Redefining Breed Identification for Pakistan's Livestock Industry

In Pakistan, livestock significantly boosts the national economy, contributing not only to rural income but also to national exports. However, identifying and classifying livestock breeds accurately and swiftly remains a challenge, primarily due to the high costs and lengthy timelines of conventional genomics-based methods. With a clear demand for an efficient and cost-effective solution, we’re pioneering a Livestock Breed Identification System that harnesses the power of AI, large vision models, and supercomputing.

This advanced, cloud-based system employs Latent Variable Models (LVMs), Transformers, and Convolutional Neural Networks (CNNs), making breed identification accessible, fast, and highly accurate. By using the PakSupercomputing HPC resources, our model ensures real-time breed classification, a game-changer for the agricultural sector and beyond.

 

 

 

 

 

 

 

 

 

 

Project Objectives: Addressing Industry Needs with Cutting-Edge Technology

Our project has clear objectives, designed to bridge the current gap in livestock identification technology:

• Develop and Deploy LVM-Based Models: Using LVMs for precise breed classification by recognizing underlying traits.
• Implement Real-Time Image Classification: Use live camera interfaces to capture images and classify breeds on the spot.
• Ensure Cloud Accessibility: Allow stakeholders to access breed data anytime, anywhere, through a user-friendly cloud interface.
• Utilize High-Performance Computing for Scalability: Leverage PakSupercomputing HPC to handle real-time processing and ensure accuracy across a vast number of queries. Existing Methods vs. The AI-Driven Solution: Why It’s Time to Switch The current methods for breed identification heavily rely on genomics, which, while accurate, are also costly and time-intensive. Our image-based solution offers a robust alternative, significantly reducing the time and cost associated with traditional approaches while maintaining precision through AI and machine learning. System Architecture: Leveraging AI Models and High-Performance Computing Our solution is powered by leading machine learning libraries and AI architectures:
• Latent Variable Models (LVMs): Capture hidden variables that influence breed characteristics.
• Transformers: Process complex data sequences to improve model accuracy.
• CNNs (Convolutional Neural Networks): Extract key features from livestock images—such as color, texture, and body structure—ensuring detailed breed classification.

We have trained our models on a comprehensive image dataset sourced from dairy farms across Pakistan, including Hafiz Dairy Farm and Rakh Gulaman. This localized data enhances model accuracy by understanding regional livestock traits. The system will be deployed on the Namal HPC Cluster, allowing high-speed processing essential for real-time applications.

Applications: A Solution with Wide-Ranging Benefits

The Livestock Breed Identification System has several impactful applications:

• Agricultural and Livestock Management: Aids in real-time breed classification, supporting efficient livestock tracking and breeding management.
• Export and Trade: Enhances livestock value and quality control for exports, enabling compliance with international breed standards.
• Research and Development: Supports researchers in identifying and studying breed-specific traits, contributing to advancements in genetic research and breeding programs.

 

Data Flow and System Control: Efficient and Real-Time Processing

The system follows a streamlined workflow:

• Image Capture: A live camera interface captures images of livestock.
• Data Preprocessing: Images undergo preprocessing for optimal model input.
• Model Execution: The high-performance models (LVMs, Transformers, CNNs) analyze data and classify breeds.
• Output and Ranking: Results are displayed in real-time, ranking breeds based on user queries, with a clear visual output for easy interpretation.

The Cloud Application online link

http://119.156.30.83:8502/

 

Team Leader :
          Dr. Tassadaq Hussain

Usman Research Assistant

•     Education
  • Electronics
    • Very-large-scale integration (VLSI) Design
    • FPGA Based Systems Design
    • Embedded System & Applications
    • Ineternet of Things
    • Multi-core Processor System
    • System On Chip Design
    • High Level Sythesis
    • Computer Architecture
  • Computing & Artificial Intelligence
    • Data Structure and Algorithm
    • Advanced Computer Vision
    • Introduction to Programming and Computing
    • Introduction Parallel Programming Models
    • Parallel Processor System
    • High Performance Computing and Applications
    • Big Data and Deep Learning
    • Real-time Artificial Intelligence based Systems
    • Supercomputing for AI and BigData
    • MachineLearning
    • AICT
  • Telecommunication
    • Telecom Systems
  • Power Generation
    • Mechanical Systems
    • Renewable Energy
• Research
  • Supercomputing System
  • Software Defiend Radio
  • Iridology
  • Medical Signal Processing
  • Digital System Design
  • Data Acquisition
  • Embedded Computer Vision
  • Processor based System
  • Real-time Augmented/Virtual Reality
  • Rehabilitation Technology
  • Cybersecurity
• Development
  • A Scalable Heterogeneous Supercomputer
  • Rice Analyzer
  • UCERD: FPGA-Powered Supercomputer
  • High Performance Software Defined Radio
  • Biomedical Application Processing System
  • Smart Car Motor Controller
  • A High Performance Real-time Interferometry Sensor
  • Iris based Disease Diagnostic System
  • Electroencephalography (EEG) based Real-time System
  • Data Acquisition System
  • An Intelligent Digital System for Foot Weight Distribution
  • Thermal imaging camera for medical applications
  • VR for Tele-Rehab
  • Soil Analyzer
  • LiveStock Breed Identification
  • Docuemnt Analyzer
• Entrepreneurship
  • Trainings & Workshops
    • RISCV Summer School
    • Supercomputing System and Artificial Intelligence
    • Software Defined Radio
    • Single Board Computer
    • Iridology for Health-care
    • Embedded Computer Vision
    • Python for Signal and Image Processing
    • Embedded System on Chip Design
    • Internet of Things
    • Write a Winning Grants Proposal
    • Ecommerce
    • Creative Writing for Vlog
    • Embedded System
    • Writing Paper in 10 Days
• News & Achievements
  • Seminar Supercomputing for AI
  • SATHA Award
  • DICE Award
  • Interferometry Sensor System Architecture
  • Biomedical Application Processing System
  • Supercomputing in Pakistan
  • OIC-COMSTECH Workshop
  • Job Vacancies