ArticleRoyal Society open science2020
Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of artificial intelligence in small ruminant production systems: applications, performance outcomes, and reported implementation challenges.BMC veterinary research · 2026Pooled it
- Precision Livestock Farming Technologies for Sheep Welfare in Extensive Systems: A Comprehensive Review.Veterinary medicine and science · 2026Review
- Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Predicting genetic merit in Harnali sheep using machine learning techniques.Tropical animal health and production · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle.PloS one · 2025Article
- Video dataset of sheep activity for animal behavioral analysis via deep learning.Data in brief · 2024Article
- Article
- Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data.Sensors (Basel, Switzerland) · 2023Article
- Exploring the potential of using simulation games for engaging with sheep farmers about lameness recognition.Frontiers in veterinary science · 2023Article
- Optimal lameness induction model development using amphotericin B in meat goats.Translational animal science · 2023Article
- Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources.Animals : an open access journal from MDPI · 2022Article
- Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs.Animals : an open access journal from MDPI · 2022Article
- Affective State Recognition in Livestock-Artificial Intelligence Approaches.Animals : an open access journal from MDPI · 2022Review
- Computer Vision for Detection of Body Posture and Behavior of Red Foxes.Animals : an open access journal from MDPI · 2022Article
- What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Do we automatically detect health- or general welfare-related issues? A framework.Proceedings. Biological sciences · 2021Article
- 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 · 2020Article
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Authors and funding
6 authors.
Funding
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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. 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 an 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.