Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours
Journal article
Hu, Shuwen, Ingham, Aaron, Schmoelzl, Sabine, McNally, Jody, Little, Bryce, Smith, Daniel, Bishop-Hurley, Greg, Wang, You-Gan and Li, Yutao. (2020). Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Computers and Electronics in Agriculture. 179, p. Article 105857. https://doi.org/10.1016/j.compag.2020.105857
Authors | Hu, Shuwen, Ingham, Aaron, Schmoelzl, Sabine, McNally, Jody, Little, Bryce, Smith, Daniel, Bishop-Hurley, Greg, Wang, You-Gan and Li, Yutao |
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Abstract | Inertial motion sensors located on the animal have been used to study the behaviour of ruminant livestock. The time window size of segmented signal data can significantly affect the classification accuracy of animal behaviours. To date, there have been no studies evaluating the impact of a mixture of time window size features on the accuracy of animal behaviour classification. In this study, data was collected from accelerometers attached to the neck of 17 Merino sheep over a period of two days. We also recorded a ground truth dataset of behaviour recordings (grazing, ruminating, walking, and standing) over the same time period, We then investigated the ability of three machine learning (ML) approaches, Random Forest (RF), Support Vector Machine (SVM) and linear discriminant analysis (LDA), to accurately classify sheep behaviour. Our results clearly show that simultaneous inclusion of features derived from time windows of mixed sizes, ranging from 2 to 15 s, significantly improved the behaviour classification accuracy, in comparison to those determined from a single unique time window size. Of the three ML methods applied here, the RF approach yielded the best results. Together our results show that including features obtained from mixed window sizes improved the classification accuracy of sheep behaviours. |
Keywords | mixture of time window sizes; features ranking; classification algorithm; machine learning; sheep behaviour; accelerometer |
Year | 2020 |
Journal | Computers and Electronics in Agriculture |
Journal citation | 179, p. Article 105857 |
Publisher | Elsevier B.V. |
ISSN | 0168-1699 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compag.2020.105857 |
Scopus EID | 2-s2.0-85094905224 |
Open access | Published as green open access |
Page range | 1-11 |
Funder | Australian Research Council (ARC) |
Author's accepted manuscript | License File Access Level Open |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 03 Nov 2020 |
Publication process dates | |
Accepted | 26 Oct 2020 |
Deposited | 17 Nov 2023 |
ARC Funded Research | This output has been funded, wholly or partially, under the Australian Research Council Act 2001 |
Grant ID | DP160104292 |
https://acuresearchbank.acu.edu.au/item/8zz39/inclusion-of-features-derived-from-a-mixture-of-time-window-sizes-improved-classification-accuracy-of-machine-learning-algorithms-for-sheep-grazing-behaviours
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Author's accepted manuscript
AM_Hu_2020_Inclusion_of_features_derived_from_a.pdf | |
License: CC BY-NC-ND 4.0 | |
File access level: Open |
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