Lameness detection of dairy cows based on a double normal background statistical model

Abstract

Dairy cow lameness is one of the diseases that affect the health of dairy cows, and the rapid and accurate detection of dairy cow lameness is an important research field for modern dairy cow farming. In this study, a lameness detection method based on a double normal distribution statistical model was proposed. The combination of these features could be used to determine the lameness of dairy cows. To verify the validity of the lameness detection algorithm proposed in this research, the detection accuracy (D-Accuracy), detection accuracy of lame (L-Accuracy), and detection accuracy of non-lame cows (N-Accuracy) were used as indices to evaluate the algorithm performance. The results showed that the total correct rate of D-Accuracy was 93.75%, that of L-Accuracy was 90.00%, and that of N-Accuracy was 100.00%. Compared with the typical GMM algorithm, the proposed algorithm was more suitable for detecting dairy cow lameness. The results also show that the robustness against the environment was stronger and that the false positive rate of the target dairy cows was reduced by 18.71%. This research has important significance for the early detection and treatment of dairy cow lameness, which could be used to keep production level high and guarantee good animal welfare.