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Automated Classification System Based on YOLO Architecture for Body Condition Score in Dairy Cows.

Emre Dandıl1, Kerim Kürşat Çevik2, Mustafa Boğa3

  • 1Department of Computer Engineering, Faculty of Engineering, Bilecik Şeyh Edebali University, Bilecik 11230, Turkey.

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Summary

This study introduces an AI system for automatically classifying dairy cow body condition scores (BCS) using YOLOv8x. The system accurately assesses cow welfare and health, aiding in early detection of metabolic issues and improving farm management.

Keywords:
YOLOv8automatic classificationbody condition scoredairy cowsdeep learning

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Area of Science:

  • Animal Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Body condition score (BCS) is crucial for dairy cow welfare, but traditional assessment relies on subjective expert observation.
  • Deviations in BCS can lead to metabolic diseases, reduced productivity, and economic losses in dairy herds.

Purpose of the Study:

  • To develop and evaluate an automated system for classifying dairy cow BCS using deep learning.
  • To improve the accuracy and efficiency of BCS assessment in dairy farming.

Main Methods:

  • An original dataset of cow images (Holstein and Simmental breeds) was created, categorized into five BCS classes: Emaciated, Poor, Good, Fat, and Obese.
  • The YOLOv8x deep learning architecture was employed for image classification.
  • Experimental analysis was conducted on the prepared dataset.

Main Results:

  • The YOLOv8x system correctly classified BCS for 102 out of 126 cow images in the test set.
  • An average accuracy of 0.81 was achieved across all BCS classes for both breeds.
  • The average area under the precision-recall curve was 0.87.

Conclusions:

  • The proposed automated BCS classification system enables accurate and rapid monitoring of dairy cow body condition.
  • This tool can assist production decision-makers, particularly in early lactation, to mitigate negative energy balance and enhance herd management.