💡 AI Technical Summary: YOLOv8 Gait Scoring

  • The Challenge: Manual gait scoring requires observing individual broilers on a 0 to 5 scale (Kestin et al.). In 30,000-bird commercial houses, manual auditing is labor-prohibitive.
  • YOLOv8 Detection Precision: Overhead RGB cameras combined with YOLOv8 bounding box tracking achieve 88.7% to 94.2% mean Average Precision (mAP@0.5) in identifying severely lame (Gait Score ≥ 3) birds.
  • Behavioral Indicators: Tracks reduced walking speed (< 0.05 m/s), increased sitting frequency (> 85% of time), and asymmetric body swaying during locomotion.

1. The Economic & Welfare Cost of Poultry Lameness

Rapid growth rates in commercial broilers put immense pressure on leg skeletal structure. Lame birds experience pain, reduce their visits to feeders, and suffer lower final slaughter weights. Early automated detection enables targeted culling or litter quality adjustments before pen-wide mortality increases.

2. Neural Network Pipeline Architecture

Overhead 4K cameras capture top-down video feeds. The YOLOv8 model detects individual bird centroids, passing coordinate streams to a DeepSORT tracking algorithm that computes individual velocity vectors and resting interval durations.

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