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This study introduces a new cattle identification system using RGB cameras, YOLOv8 for detection, and VGG with SVM for accurate recognition. This method overcomes limitations of traditional ear tags for improved farm management.

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Area of Science:

  • Agricultural Technology
  • Computer Vision
  • Animal Science

Background:

  • Automated cattle monitoring is crucial for farm management, requiring reliable individual animal identification.
  • Existing methods like ear tags are prone to loss or damage, causing financial issues for farmers.
  • Real-time, non-invasive identification is needed for efficient cow health and welfare assessment.

Purpose of the Study:

  • To develop and validate a novel, robust cattle identification system using RGB image-based technology.
  • To address the limitations of traditional cattle identification methods in farm management.
  • To demonstrate the efficacy of combining deep learning and machine learning for automated cattle recognition.

Main Methods:

  • Cattle detection using the YOLOv8 (You Only Look Once) model in video footage.
  • Real-time tracking of detected cattle, assigning unique local IDs.
  • Feature extraction using the VGG (Visual Geometry Group) model.
  • Classification and identification using the SVM (Support Vector Machine) classifier.

Main Results:

  • The proposed system successfully tracks and identifies individual cattle in real-time.
  • Combining VGG features with SVM demonstrated a promising approach for automated cattle identification.
  • The method provides a proof of concept for a reliable, tag-less cattle identification solution.

Conclusions:

  • The developed RGB image-based system offers a viable alternative to traditional ear tags for cattle identification.
  • This technology can significantly enhance automated cattle monitoring and farm management systems.
  • Further development could lead to widespread adoption in precision agriculture for improved animal welfare.