Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep

Kaler, Jasmeet; Mitsch, Jurgen; V�zquez-Diosdado, Jorge A.; Bollard, Nicola; Dottorini, Tania; Ellis, Keith A.

Abstract

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Use of accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.

Citation

Kaler, J., Mitsch, J., Vázquez-Diosdado, J. A., Bollard, N., Dottorini, T., & Ellis, K. A. (2020). Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep. Royal Society Open Science, 7(1), Article 190824. https://doi.org/10.1098/rsos.190824

• Journal Article Type: Article
• Acceptance Date: Nov 29, 2019
• Online Publication Date: Jan 15, 2020
• Publication Date: 2020-01
• Deposit Date: Dec 19, 2019
• Publicly Available Date: Jan 15, 2020
• Journal: Royal Society Open Science
• Electronic ISSN: 2054-5703
• Publisher: The Royal Society
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Article Number: 190824
• DOI: https://doi.org/10.1098/rsos.190824
• Keywords: Sheep behaviour, Lameness, Machine learning, Accelerometer and gyroscope, Sensor, Signal processing, Precision livestock farming
• Public URL: https://nottingham-repository.worktribe.com/output/3600918
• Publisher URL: https://royalsocietypublishing.org/doi/10.1098/rsos.190824
• Additional Information: Received: 2019-05-14; Accepted: 2019-11-29; Published: 2020-01-15