Sensor Data Classification for the Indication of Lameness in Sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed at determining the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep.

[1]  Thomas Bak,et al.  ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees , 2008 .

[2]  Greg Bishop-Hurley,et al.  Behavioral classification of data from collars containing motion sensors in grazing cattle , 2015, Comput. Electron. Agric..

[3]  Toby T Mottram,et al.  A Novel Method of Monitoring Mobility of Dairy Cows , 2010 .

[4]  M. Pastell,et al.  A wireless accelerometer system with wavelet analysis for assessing lameness in cattle. , 2009 .

[5]  J. P. Holland,et al.  An automated sensor-based method of simple behavioural classification of sheep in extensive systems , 2008 .

[6]  M. Kolehmainen,et al.  Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines , 2009 .

[7]  Ying Guo,et al.  Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model , 2009 .

[8]  Daniel Berckmans,et al.  Online lameness detection in dairy cattle using Body Movement Pattern (BMP) , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[9]  Thomas Niesler,et al.  Automatic classification of sheep behaviour using 3-axis accelerometer data , 2015 .

[10]  L. Munksgaard,et al.  Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms. , 2015, Animal : an international journal of animal bioscience.

[11]  Bart Sonck,et al.  Lameness Detection in Dairy Cows: Part 1. How to Distinguish between Non-Lame and Lame Cows Based on Differences in Locomotion or Behavior , 2015, Animals : an open access journal from MDPI.

[12]  J Rushen,et al.  Measurement of acceleration while walking as an automated method for gait assessment in dairy cattle. , 2011, Journal of dairy science.

[13]  D M Weary,et al.  Gait assessment in dairy cattle. , 2009, Animal : an international journal of animal bioscience.

[14]  Daniel Berckmans,et al.  Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows , 2014 .

[15]  D Berckmans,et al.  Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle. , 2013, Journal of dairy science.

[16]  Christian Rutz,et al.  Reality mining of animal social systems. , 2013, Trends in ecology & evolution.

[17]  Daniel Berckmans,et al.  Evaluation of Step Overlap as an Automatic Measure in Dairy Cow Locomotion , 2010 .

[18]  V. H. Oddy,et al.  Using a three-axis accelerometer to identify and classify sheep behaviour at pasture , 2016 .

[19]  Greg Bishop-Hurley,et al.  Dynamic cattle behavioural classification using supervised ensemble classifiers , 2015, Comput. Electron. Agric..

[20]  Claudia Bahr,et al.  Original paper: Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques , 2010 .

[21]  E. Aizinbud,et al.  A field investigation of the use of the pedometer for the early detection of lameness in cattle. , 2006, The Canadian veterinary journal = La revue veterinaire canadienne.

[22]  Daniel Berckmans,et al.  Automatic detection of lameness in dairy cattle-Vision-based trackway analysis in cow's locomotion , 2008 .

[23]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[24]  K. Anzuino,et al.  A case report of lameness in two dairy goat herds; a suspected combination of nutritional factors concurrent with treponeme infection , 2015, BMC Research Notes.

[25]  Corrado Dimauro,et al.  Automatic classification system for grazing, ruminating and resting behaviour of dairy sheep using a tri-axial accelerometer , 2017 .

[26]  David G. Renter,et al.  Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle , 2009 .

[27]  Jian Pei,et al.  2012- Data Mining. Concepts and Techniques, 3rd Edition.pdf , 2012 .

[28]  Agnes Winter,et al.  Lameness in Sheep , 2004 .

[29]  Edward A. Codling,et al.  Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system , 2015, Animal Biotelemetry.

[30]  Herbert H. T. Prins,et al.  Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat , 2015, PloS one.

[31]  L. Plümer,et al.  Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior , 2012 .

[32]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[33]  Eduardo C. B. Romanini,et al.  Improving a computer vision lameness detection system by adding behaviour and performance measures , 2014 .

[34]  J. Praks,et al.  IceTag3DTM accelerometric device in cattle lameness detection. , 2014 .

[35]  H. Søgaard,et al.  ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees , 2008 .