Al-Rubaye, Zainab

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Sheep Lameness Detection via Wearable Sensor-Based Data Analysis

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Sheep Lameness Detection via          Wearable Sensor-Based Data AnalysisZainab Al-Rubaye, Ali Al-Sherbaz, Wanda McCormick, Scott TurnerContact email: Zainab.al-rubaye@Northampton.ac.ukWhat is lameness? A painful erratic movement caused by infectious bacteria that grow in the soil and can easily transfer to the sheep’s foot. It results in the animal deviating from its normal gait or posture.▪ Footrot is responsible for 90% of sheep lameness in the UK due to climate changes between mild winter and wet summer. Research aim: Utilising sensor devices to detect the early signs of lameness by collecting the movement measurements of the mounted sensor around the sheep’s neck. The collected data were analysed to classify the sheep into the lame and sound classes via machine learning approaches. AHDB estimated the annual loss to the British sheep industry to be £10 for each ewe (for footrot alone) in 2016.ADAS reported that each lame ewe costed £89.80 in 2013.Research impact: One of the major health and welfare concerns for the sheep industry in the UK is lameness that leads to a huge economic problem and causes a reduction in overall farm productivity.xzData Collection:Divide the raw data into Train set & Data setBuild a classification model (decision tree) using Train data setEvaluate the build model using the Test data setMethodology: Initial Results: Conclusion:The Decision Tree classifier can identify lame from non-lame sheep by depending on the Roll angle measurements of y-axis. 

http://nectar.northampton.ac.uk/12262/1/Al_Rubaye_etal_2018_Sheep_Lameness_Detection_via_Wearable_Sensor_Based_Data_Analysis.pdf

Sheep Lameness Detection via Wearable Sensor-Based Data Analysis

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