How Now Lame Cow: Automatic Lameness Assessment for Dairy Cattle with 3D Sensors

Lameness in dairy cattle is a prevalent health issue impacting animal welfare and economic performance. Automatic lameness detection using 3D sensors is proposed in this thesis to automate the current time intensive manual locomotion scoring, resulting in more objective and frequent monitoring of individual lameness. Conventional visual locomotion scoring was analysed for consistency amongst observers, providing an estimate for human locomotion scoring performance and quantifying the ground truth consistency for an automatic lameness detection system. A 4-level scale had to be binarised to 2 levels to achieve consistent locomotion scoring from two expert observers. Novice observers achieved consistent scoring on a 4-level scale, and performed pairwise preference scoring with good consistency, where observers scored which of two passings was more lame. 3D sensors to record cattle kinematics were evaluated on data quality, positioning, and cost. A prototype with overhead and side-on Kinect-v2 sensors was developed, and locomotor keypoints were detected in 2D. Tracking, in particular of hooves and carpal/tarsal joints, was performed after projection of detections into 3D. Gait metrics were extracted from the resulting keypoint trajectories including a variety of hoof placement, spine curvature, and hip displacement metrics. The difference between gait metrics at different scores, and the repeatability of measurement were analysed. Gait metrics were used to perform regression, classification, and pairwise preference prediction of ground truth scores. A simple neural network using stacked gait metrics achieved near perfect performance for pairwise preference prediction on 28613 pairs (se. = 0.99, sp. = 0.98), showing that automatic classification of differences in gait performs better than predicting an absolute score. This thesis proposes a new research direction for lameness detection by rethinking (automatic) lameness monitoring as a relative measurement instead of absolute measurement. Both manual pairwise preference scoring and classification of pairs have shown encouraging results towards the ultimate goal of accurate on-farm automatic lameness monitoring