Automatic lameness detection in dairy cattle based on leg swing analysis with an image processing technique

摘要

Lameness has become a frequent and serious problem for herd productivity and animal welfare in the dairy industry. As the most significant characteristic of lameness, gait characteristics have been used to estimate lameness by human experts. The objective of this study was to analyze leg swing using computer vision techniques and to develop an automatic and continuous system for scoring the locomotion of cows to detect and predict lameness with high accuracy and practicability. The focus was on quantifying the movement pattern of cows and demonstrating the possibility of classifying lameness using the features extracted from movement analysis. Side-view videos were recorded after the cows were milked. Cows were scored by an expert on a scale from 1 (sound) to 3 (severely lame). The data set included 621 videos from 98 cows. The motion curve was plotted by extracting the position of the moving leg by image processing, and the motion curve was analyzed to generate six features referring to the gait asymmetry, speed, tracking up, stance time, stride length, and tenderness. A box-plot of the features within 3 classes showed that the dataset was nearly linear and separable under the six features and that the cows had different lameness indicators in different lameness stages. The Decision Tree classifier was applied to the dataset, and 2-, 3-, and 10-fold cross validation was used to verify the performance of the algorithm. The accuracy of the classification was 90.18%, and the averages of sensitivity and specificity were 90.25% and 94.74%, respectively. This research demonstrates the feasibility of classifying dairy cow lameness based on the six motion features extracted by leg swing analysis.