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Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers

Said Benaissa (UGent) , Frank Tuyttens (UGent) , David Plets (UGent) , Hannes Cattrysse, Luc Martens (UGent) , Leen Vandaele, Wout Joseph (UGent) and Bart Sonck (UGent)
(2019) APPLIED ANIMAL BEHAVIOUR SCIENCE. 211. p.9-16
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Abstract
A new simple decision-tree (DT) algorithm was developed using the data from a neck-mounted accelerometer for real-time classification of feeding and ruminating behaviours of dairy cows. The performance of the DT was compared to that of a support vector machine (SVM) algorithm and a RumiWatch noseband sensor and the effect of decreasing the sampling rate of the accelerometer on the classification accuracy of the developed algorithms was investigated. Ten multiparous dairy cows were used in this study. Each cow was fitted with a RumiWatch halter and an accelerometer attached to the cow's collar with both sensors programmed to log data at 10 Hz. Direct observations of the cows' behaviours were used as reference (baseline data). Results indicate that the two sensors have similar classification performances for the considered behavioural categories (i.e., feeding, ruminating, other activity), with an overall accuracy of 93% for the accelerometer with SVM, 90% for the accelerometer with DT, and 91% for the Rumiwatch sensor. The difference between the predicted and the observed ruminating time (in min/h) was less than 1 min. h (1.5% of the observed time) for the SVM and less than 2 min. h (2.8%) for both DT and the RumiWatch. Similarly, the difference in feeding time was 1.3 min. h (2.1%) for the SVM compared to 2.5 min. h (4.3%) and 2.4 min. h (4.1%) for both RumiWatch and DT, respectively. These preliminary findings illustrate the potential of the collar-mounted accelerometer to classify feeding and ruminating behaviours with accuracy measures comparable to the Rumiwatch noseband sensor.
Keywords
Behaviors classification, Accelerometer, RumiWatch, Dairy cows, Machine learning, Internet-of-animals, DAIRY-COWS, FEEDING-BEHAVIOR, RUMINATION ACTIVITY, PRESSURE SENSOR, LAMENESS, TIME, VALIDATION, SYSTEM, ACCELERATION, GHZ

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Citation

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MLA
Benaissa, Said, et al. “Classification of Ingestive-Related Cow Behaviours Using RumiWatch Halter and Neck-Mounted Accelerometers.” APPLIED ANIMAL BEHAVIOUR SCIENCE, vol. 211, 2019, pp. 9–16, doi:10.1016/j.applanim.2018.12.003.
APA
Benaissa, S., Tuyttens, F., Plets, D., Cattrysse, H., Martens, L., Vandaele, L., … Sonck, B. (2019). Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers. APPLIED ANIMAL BEHAVIOUR SCIENCE, 211, 9–16. https://doi.org/10.1016/j.applanim.2018.12.003
Chicago author-date
Benaissa, Said, Frank Tuyttens, David Plets, Hannes Cattrysse, Luc Martens, Leen Vandaele, Wout Joseph, and Bart Sonck. 2019. “Classification of Ingestive-Related Cow Behaviours Using RumiWatch Halter and Neck-Mounted Accelerometers.” APPLIED ANIMAL BEHAVIOUR SCIENCE 211: 9–16. https://doi.org/10.1016/j.applanim.2018.12.003.
Chicago author-date (all authors)
Benaissa, Said, Frank Tuyttens, David Plets, Hannes Cattrysse, Luc Martens, Leen Vandaele, Wout Joseph, and Bart Sonck. 2019. “Classification of Ingestive-Related Cow Behaviours Using RumiWatch Halter and Neck-Mounted Accelerometers.” APPLIED ANIMAL BEHAVIOUR SCIENCE 211: 9–16. doi:10.1016/j.applanim.2018.12.003.
Vancouver
1.
Benaissa S, Tuyttens F, Plets D, Cattrysse H, Martens L, Vandaele L, et al. Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers. APPLIED ANIMAL BEHAVIOUR SCIENCE. 2019;211:9–16.
IEEE
[1]
S. Benaissa et al., “Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers,” APPLIED ANIMAL BEHAVIOUR SCIENCE, vol. 211, pp. 9–16, 2019.
@article{8611134,
  abstract     = {{A new simple decision-tree (DT) algorithm was developed using the data from a neck-mounted accelerometer for real-time classification of feeding and ruminating behaviours of dairy cows. The performance of the DT was compared to that of a support vector machine (SVM) algorithm and a RumiWatch noseband sensor and the effect of decreasing the sampling rate of the accelerometer on the classification accuracy of the developed algorithms was investigated. Ten multiparous dairy cows were used in this study. Each cow was fitted with a RumiWatch halter and an accelerometer attached to the cow's collar with both sensors programmed to log data at 10 Hz. Direct observations of the cows' behaviours were used as reference (baseline data). Results indicate that the two sensors have similar classification performances for the considered behavioural categories (i.e., feeding, ruminating, other activity), with an overall accuracy of 93% for the accelerometer with SVM, 90% for the accelerometer with DT, and 91% for the Rumiwatch sensor. The difference between the predicted and the observed ruminating time (in min/h) was less than 1 min. h (1.5% of the observed time) for the SVM and less than 2 min. h (2.8%) for both DT and the RumiWatch. Similarly, the difference in feeding time was 1.3 min. h (2.1%) for the SVM compared to 2.5 min. h (4.3%) and 2.4 min. h (4.1%) for both RumiWatch and DT, respectively. These preliminary findings illustrate the potential of the collar-mounted accelerometer to classify feeding and ruminating behaviours with accuracy measures comparable to the Rumiwatch noseband sensor.}},
  author       = {{Benaissa, Said and Tuyttens, Frank and Plets, David and Cattrysse, Hannes and Martens, Luc and Vandaele, Leen and Joseph, Wout and Sonck, Bart}},
  issn         = {{0168-1591}},
  journal      = {{APPLIED ANIMAL BEHAVIOUR SCIENCE}},
  keywords     = {{Behaviors classification,Accelerometer,RumiWatch,Dairy cows,Machine learning,Internet-of-animals,DAIRY-COWS,FEEDING-BEHAVIOR,RUMINATION ACTIVITY,PRESSURE SENSOR,LAMENESS,TIME,VALIDATION,SYSTEM,ACCELERATION,GHZ}},
  language     = {{eng}},
  pages        = {{9--16}},
  title        = {{Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers}},
  url          = {{http://doi.org/10.1016/j.applanim.2018.12.003}},
  volume       = {{211}},
  year         = {{2019}},
}
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