Multivariable sensor data to early detect lameness in sheep

Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D., Turner, S. J. and Ghendir, S (2016) The use of multivariable sensor data to early detect lameness in sheep. Paper presented to: Sensors in Food and Agriculture, Møller Centre, Churchill College, University of Cambridge, 29-30 November 2016.

Abstract

Lameness is a clinical symptom referring to locomotion changes that widely differ from normal gait or posture. Lameness has a negative impact on both farm productivity and sheep welfare. The annual loss to the British sheep industry, because of the footrot only ;which is one of the common lameness causes, is estimated by £10 for each ewe. 

Since lameness is often an infectious disease that can be easily spread¬ within the flock, the prior detection of the lame sheep will be expected to decrease the prevalence of lameness and enabling the shepherd to react quickly to the better treatment. 

The prototype sensor has been developed primarily to conduct this research, offering an automatic monitoring of individual sheep to collect behavioural data measurements from a precise sensor that mounted within a neck collar. The sensor variables include 3-axis acceleration, 3-axis Gyroscope, (Roll, Pitch, Heading) angles, longitude, latitude and time. 

The sensor parameters were be used as inputs to data analysis algorithms. The preliminary results from applying pre-existing classification algorithms gave a positive indication for earlier lameness detection, however; the next experiments aim to simplify the process of lameness detection by eliminating the least effective parameters

All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Zainab's prize winning poster

Lameness Detection in Sheep Through Behavourial Sensor Data Analysis

Zainab Al-Rubaye

Abstract
Lameness is a clinical symptom of the painful disorder, which refers to the locomotion changes in sheep movement. These unbalanced movements result in a deviation from normal gait or posture. The footrot is considered one of the most significant causes of lameness in sheep in Great Britain due to a bacteria grows in a mud soil which transfer to the sheep foot and cause footrot that leads to lameness. Lameness has a negative impact on both sheep welfare and farm economy. The annual loss from the footrot only is estimated by £6 for each ewe in Great Britain according to the statistics from Agriculture and Horticulture Development Board (AHDB) in 2014. Therefore, preclinical detection of lameness at the farm will increase the level of protection regarding sheep health and farm commerce decline. The newly developed sensor technology utilises the idea of automatically monitoring objects either human or animal to determine the physiological and behavioural indicators, which are subsequently used an input to data analysis algorithms. The automated methods to monitor the farm bring many advantages to the farmer in terms of time spending, flock size increasing and sensitivity to detect the lamenessThe type of the collected data from the sensor used for recording animal’s behaviour depend on the sensor’s features and functionality. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides 3-aix acceleration, 3-aix angular velocity, 3-aix angles (Roll, Pitch, and Heading), longitude, latitude and time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to early detect lameness in sheep by analysing the data that will be retrieved from a mounted sensor on the sheep neck collar. This extensive spatio-temporal data will be classified to infer the associated behaviour to the lame sheep according to an efficient data mining learning techniques. This model will help the shepherd to early detect the lame sheep to prevent the worse situation of trimming or even culling the sheep.

For more details go to http://nectar.northampton.ac.uk/8311/


Supervisory team:
Dr Ali Al-Sherbaz
Dr Wanda McCormick
Dr Scott Turner


If you'd like to find out more about Computing at the University of Northampton go to: www.computing.northampton.ac.uk. All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with