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Open AccessArticle

A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming

1
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK
2
Internet of Things Systems Research, Intel Labs, W23 CX68 Leixlip, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3201; https://doi.org/10.3390/s19143201
Received: 30 June 2019 / Revised: 16 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions. View Full-Text
Keywords: algorithm; concept drift; Precision Livestock Farming; machine learning; animal behaviour algorithm; concept drift; Precision Livestock Farming; machine learning; animal behaviour
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MDPI and ACS Style

Vázquez-Diosdado, J.A.; Paul, V.; Ellis, K.A; Coates, D.; Loomba, R.; Kaler, J. A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming. Sensors 2019, 19, 3201. https://doi.org/10.3390/s19143201

AMA Style

Vázquez-Diosdado JA, Paul V, Ellis KA, Coates D, Loomba R, Kaler J. A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming. Sensors. 2019; 19(14):3201. https://doi.org/10.3390/s19143201

Chicago/Turabian Style

Vázquez-Diosdado, Jorge A.; Paul, Veronica; Ellis, Keith A; Coates, David; Loomba, Radhika; Kaler, Jasmeet. 2019. "A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming" Sensors 19, no. 14: 3201. https://doi.org/10.3390/s19143201

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