Current methods for assessing breeding sow health rely on manual, labor-intensive data collection and subjective evaluation, leading to inefficiencies in farm management and potentially impacting animal welfare. The lack of accurate, predictive tools hinders proactive decision-making regarding breeding selection and culling.

Motion Grazer AI offers a livestock monitoring system that employs depth-sensing cameras and machine learning to analyze the shape and gait of breeding sows. By tracking joint locations and assessing body condition, the system provides quantitative data for predicting lameness and overall health. This technology enables farmers to make informed decisions about breeding and culling, optimizing herd management and improving animal welfare. The system's AI-driven analysis replaces subjective assessments with objective measurements, leading to more efficient and data-driven farm operations.

Utilizes depth analysis to determine sow shape and condition from snout to tail.

Tracks 3D joint locations to evaluate sow gait.

Employs convolutional neural networks trained on extensive datasets for accurate analysis.

Provides quantitative data to predict lameness and body condition.

Offers a low-cost unit (SIMKit) for capturing changes in body composition.

Automates data collection, saving labor time and person power.

The primary target audience includes pig farmers and agricultural operations focused on breeding sows, seeking to improve farm efficiency, enhance animal welfare, and increase productivity through data-driven insights.