Analysis of Calving Cow Posture Recognition, Behavioral Changes, and Influencing Factors Based on Machine Vision.

Area of Science:

• Animal Science
• Computer Vision
• Artificial Intelligence

Background:

• Monitoring dairy cow calving is crucial for animal welfare and farm productivity.
• Traditional methods for observing calving behavior are labor-intensive and may lack precision.

Purpose of the Study:

• To develop and validate a non-contact, real-time monitoring system for dairy cow calving posture and behavior.
• To analyze behavioral differences between primiparous and multiparous cows during parturition.

Main Methods:

• Utilized the YOLOv8 model for posture and behavior recognition from 600 video recordings (10,544 image samples).
• Analyzed calving behavior in 86 dairy cows (30 primiparous, 56 multiparous).

Main Results:

• YOLOv8 model achieved high performance (precision 96.72%, recall 96.53%, mAP 97.41%).
• Identified distinct behavioral patterns: primiparous cows showed more frequent posture transitions and longer parturition times.
• Calf birth weight positively correlated with maternal behaviors, particularly in primiparous cows.

Conclusions:

• The YOLOv8-based system accurately recognizes dairy cow calving postures and behaviors.
• Provides a foundation for intelligent farm management and early detection of calving abnormalities.
• Offers insights into parturition behavior differences between first-time and experienced mothers.