Citation: Pavlovic, D.; Czerkawski,
M.; Davison, C.; Marko, O.; Michie,
C.; Atkinson, R.; Crnojevic, V.;
Andonovic, I.; Rajovic, V.; Kvascev,
G.; et al. Behavioural Classification of
Cattle Using Neck-Mounted
Accelerometer-Equipped Collars.
Sensors 2022, 22, 2323. https://
doi.org/10.3390/s22062323
Academic Editors: Dionysis Bochtis
and Aristotelis C. Tagarakis
Received: 17 February 2022
Accepted: 14 March 2022
Published: 17 March 2022
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Article
Behavioural Classification of Cattle Using Neck-Mounted
Accelerometer-Equipped Collars
Dejan Pavlovic
1,
* , Mikolaj Czerkawski
2
, Christopher Davison
2
, Oskar Marko
1
, Craig Michie
2
,
Robert Atkinson
2
, Vladimir Crnojevic
1
, Ivan Andonovic
2
, Vladimir Rajovic
3
, Goran Kvascev
3
and Christos Tachtatzis
2
1
BioSense Institute, 21101 Novi Sad, Serbia; oskar.marko@biosense.rs (O.M.); crnojevic@biosense.rs (V.C.)
2
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1RD, UK;
mikolaj.czerkawski@strath.ac.uk (M.C.); christopher.davison@strath.ac.uk (C.D.);
c.michie@strath.ac.uk (C.M.); robert.atkinson@strath.ac.uk (R.A.); i.andonovic@strath.ac.uk (I.A.);
christos.tachtatzis@strath.ac.uk (C.T.)
3
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia; rajo@etf.rs (V.R.);
kvascev@etf.bg.ac.rs (G.K.)
* Correspondence: dejan.pavlovic@biosense.rs
Abstract:
Monitoring and classification of dairy cattle behaviours is essential for optimising milk
yields. Early detection of illness, days before the critical conditions occur, together with automatic
detection of the onset of oestrus cycles is crucial for obviating prolonged cattle treatments and
improving the pregnancy rates. Accelerometer-based sensor systems are becoming increasingly
popular, as they are automatically providing information about key cattle behaviours such as the
level of restlessness and the time spent ruminating and eating, proxy measurements that indicate
the onset of heat events and overall welfare, at an individual animal level. This paper reports on
an approach to the development of algorithms that classify key cattle states based on a systematic
dimensionality reduction process through two feature selection techniques. These are based on
Mutual Information and Backward Feature Elimination and applied on knowledge-specific and
generic time-series extracted from raw accelerometer data. The extracted features are then used
to train classification models based on a Hidden Markov Model, Linear Discriminant Analysis
and Partial Least Squares Discriminant Analysis. The proposed feature engineering methodology
permits model deployment within the computing and memory restrictions imposed by operational
settings. The models were based on measurement data from 18 steers, each animal equipped with
an accelerometer-based neck-mounted collar and muzzle-mounted halter, the latter providing the
truthing data. A total of 42 time-series features were initially extracted and the trade-off between
model performance, computational complexity and memory footprint was explored. Results show
that the classification model that best balances performance and computation complexity is based on
Linear Discriminant Analysis using features selected through Backward Feature Elimination. The
final model requires 1.83
±
1.00 ms to perform feature extraction with 0.05
±
0.01 ms for inference
with an overall balanced accuracy of 0.83.
Keywords: precision agriculture; cattle behaviour monitoring; feature selection
1. Introduction
Autonomous cattle behaviour monitoring systems have grown in importance over the
recent past. Sensor-based technologies are now starting to be accepted as an enhancement
to traditional visual inspection, the latter being both time-consuming and labour-intensive.
In the UK, there has been a steady decline in the number of milk producers, whilst at the
same time the average size per herd has risen as small-scale farm holdings have departed
the industry sector due to the economic pressure. The average number of cows per herd
has also grown from ~75 in 1996 to ~155 in 2020 [
1
]; and during the same period, milk
Sensors 2022, 22, 2323. https://doi.org/10.3390/s22062323 https://www.mdpi.com/journal/sensors
