An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation
Xiaofei Dai,
Guodong Cheng,
Lu Yang,
Yali Wang,
Zhongkun Li,
Shuqing Han () and
Jifang Liu
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Xiaofei Dai: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Guodong Cheng: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Lu Yang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yali Wang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Zhongkun Li: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Shuqing Han: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Jifang Liu: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Agriculture, 2025, vol. 15, issue 15, 1-18
Abstract:
This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a threshold discrimination module using variance of motion-direction acceleration was designed to distinguish states within 2 s, enabling rapid data screening. For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. Typical gait features were fused with deep features automatically learned by the network, enabling accurate discrimination among healthy, mild (early) lameness, and severe lameness. Results showed that the online detection model achieved 80.6% dairy cow health status detection accuracy with 0.8 ms single-decision latency. The recall and F1 score for lameness, including early and severe cases, reached 89.11% and 88.93%, demonstrating potential for early and progressive lameness detection. This study improves lameness detection efficiency and validates the feasibility and practical value of wearable sensor-based gait analysis for dairy cow health management, providing new approaches and technical support for monitoring and early intervention on large-scale farms.
Keywords: dairy cow lameness; early lameness detection; improved InceptionTime; feature fusion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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