Precision Livestock Farming (PLF) relies on accurate, high-frequency data to optimize production efficiency. Traditional assessments of feeding behavior remain manual and invasive, lacking the kinematic resolution required for automated control systems. This study developed and validated a novel computer vision framework integrating YOLOv8 and the Segment Anything Model (SAM) to address this gap. The objective was to engineer a non-invasive, automated pipeline to quantify high-speed broiler biomechanics in real time. The system was validated using video data from broilers across three growth stages and varying feed granulometries (fine mash, coarse mash, and pellets) to test its robustness in detecting subtle kinematic variations. The hybrid YOLO-SAM pipeline achieved high performance, with a precision of 0.95 and a recall of 0.91, confirming its reliability as a scalable sensor for smart farming platforms. Biomechanical analysis demonstrated the system’s sensitivity, showing that larger feed particles induce greater beak gape and displacement while significantly improving ingestion efficiency (0.6 effort ratio for pellets vs. 3.0 for mash). This research provides a validated technical foundation for digital phenotyping in poultry, offering a hands-free, quantitative tool that supports data-driven decision-making in feed formulation and production management.
Dionizio et al. (Fri,) studied this question.