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Sheep biometric identification based on multiple body parts.

R Biton1, I Shimshoni2, A Godo3

  • 1Precision Livestock Farming (PLF) Laboratory, Institute of Agricultural and Biosystems Engineering, Agricultural Research Organization (A.R.O.) -Volcani Institute, 68 Hamaccabim Road, P.O.B 15159, Rishon Lezion 7505101, Israel; Dept. of Information Systems, Haifa University, 199 Abba Khoushy Ave, Haifa 3498838, Israel.

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Summary

Automated sheep identification using computer vision achieved 93% accuracy by combining facial and back features with visual tag recognition. This advancement improves precision livestock farming without manual intervention.

Keywords:
Computer VisionDeep learningMultisource identificationPrecision livestock farmingSmall ruminants

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Precision livestock farming requires accurate individual animal identification for data integration.
  • Current methods like ear tags have limitations such as loss and wear.
  • Advances in computer vision offer potential for automated animal identification.

Purpose of the Study:

  • To develop an automated system for individual sheep identification during drinking visits.
  • To evaluate the effectiveness of using visual features from different body regions (face, back, legs).
  • To determine if combining multiple regions and visual tag recognition improves identification accuracy.

Main Methods:

  • Utilized computer vision and deep learning techniques for sheep identification.
  • Employed YOLOv8 for object detection (body parts, ear tags) and ResNet50 for feature extraction.
  • Integrated GLASS algorithm for visual tag text recognition.

Main Results:

  • Leg-based features alone yielded a peak accuracy of 0.64.
  • Back-based features alone achieved a peak accuracy of 0.79.
  • Combining back and face features reached 0.91 accuracy, while integrating back, face, and visual tag features resulted in 0.93 accuracy.

Conclusions:

  • Individual sheep identification is feasible using combined visual features from multiple body regions.
  • Combining predictions from different regions and visual tag recognition significantly enhances identification accuracy.
  • The developed system offers a non-invasive, automated solution for precision livestock farming.