Abstract
Animal activity recognition is in the interest of agricultural community, animal behaviorists, and conservationists since it acts as an indicator of the animal’s health in addition to their nutrition intake when the observation is performed during the circadian circle. Machine learning techniques and tools are used to help identify the activities of livestock. These techniques are helpful to discriminate between complex patterns for classifying animal behaviors during the day; human observation alone is labor intensive and time consuming. This research proposes a robust machine learning method to classify five activities of livestock. To prove the concept, a dataset was utilized based on the observation of two sheep and four goats. A feature selection technique, namely Boruta, was tested with multilayer perceptron, random forests, extreme gradient boosting, and k-Nearest neighbors algorithms. The best results were obtained with random forests achieving accuracy of 96.47% and kappa value of 95.41%. The results showed that the method can classify grazing, lying, scratching or biting, standing, and walking with high sensitivity and specificity.
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References
McLennan, K.M., et al.: Technical note: validation of an automatic recording system to assess behavioural activity level in sheep (Ovis aries). Small Rumin. Res. 127, 92–96 (2015)
Barwick, J., Lamb, D., Dobos, R., Schneider, D., Welch, M., Trotter, M.: Predicting lameness in sheep activity using tri-axial acceleration signals. Animals 8 (2018)
Shepard, E.L.C., et al.: Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2008)
Gougoulis, D.A., Kyriazakis, I., Fthenakis, G.C.: Diagnostic significance of behaviour changes of sheep: a selected review. Small Rumin. Res. 92, 52–56 (2010)
Krahnstoever, N., Rittscher, J., Tu, P., Chean, K., Tomlinson, T.: Activity recognition using visual tracking and RFID. In: Seventh IEEE Workshops on Application of Computer Vision, WACV/MOTIONS 2005, vol. 1, pp. 494–500 (2005)
Cangar, Ö., et al.: Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis. Comput. Electron. Agric. 64, 53–60 (2008)
Schlecht, E., Hülsebusch, C., Mahler, F., Becker, K.: The use of differentially corrected global positioning system to monitor activities of cattle at pasture. Appl. Anim. Behav. Sci. 85, 185–202 (2004)
Ungar, E.D., Henkin, Z., Gutman, M., Dolev, A., Genizi, A., Ganskopp, D.: Inference of animal activity from gps collar data on free-ranging cattle. Rangel. Ecol. Manag. 58, 256–266 (2005)
Schwager, M., Anderson, D.M., Butler, Z., Rus, D.: Robust classification of animal tracking data. Comput. Electron. Agric. 56, 46–59 (2007)
Giovanetti, V., et al.: Automatic classification system for grazing, ruminating and resting behaviour of dairy sheep using a tri-axial accelerometer. Livest. Sci. 196, 42–48 (2017)
González, L.A., Bishop-Hurley, G.J., Handcock, R.N., Crossman, C.: Behavioral classification of data from collars containing motion sensors in grazing cattle. Comput. Electron. Agric. 110, 91–102 (2015)
Gutierrez-Galan, D., et al.: Embedded neural network for real-time animal behavior classification. Neurocomputing 272, 17–26 (2018)
Nadimi, E.S., Jørgensen, R.N., Blanes-Vidal, V., Christensen, S.: Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks. Comput. Electron. Agric. 82, 44–54 (2012)
Kamminga, J.W., Bisby, H.C., Le, D.V., Meratnia, N., Havinga, P.J.M.: Generic online animal activity recognition on collar tags. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers on - UbiComp 2017, pp. 597–606. ACM, New York (2017)
Umstätter, C., Waterhouse, A., Holland, J.P.: An automated sensor-based method of simple behavioural classification of sheep in extensive systems. Comput. Electron. Agric. 64, 19–26 (2008)
Arcidiacono, C., Porto, S.M.C.C., Mancino, M., Cascone, G.: Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Comput. Electron. Agric. 134, 124–134 (2017)
le Roux, S.P., Marias, J., Wolhuter, R., Niesler, T.: Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis). Anim. Biotelemetry. 5, 25 (2017)
Radeski, M., Ilieski, V.: Gait and posture discrimination in sheep using a tri-axial accelerometer. Animal. 11, 1249–1257 (2017)
Le Roux, S., Wolhuter, R., Niesler, T.: An overview of automatic behaviour classification for animal-borne sensor applications in South Africa (2017)
Anderson, D.M., Estell, R.E., Holechek, J.L., Ivey, S., Smith, G.B.: Virtual herding for flexible livestock management - a review. Rangel. J. 36, 205–221 (2014)
Norton, B.E., Barnes, M., Teague, R.: Grazing management can improve livestock distribution: increasing accessible forage and effective grazing capacity. Rangelands 35, 45–51 (2013)
Rutter, S.M.: 13 - Advanced livestock management solutions. In: Ferguson, D.M., Lee, C., Fisher, A. (eds.) Advances in Sheep Welfare, pp. 245–261. Woodhead Publishing (2017)
Kamminga, J.W.: Generic online animal activity recognition on collar tags (2017)
Mitra, S.K.: Digital Signal Processing: A Computer-Based Approach. McGraw-Hill School Education Group (2001)
Rabiner, L.R., Gold, B.: Theory and Application of Digital Signal Processing (1975)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Kursa, M.B., Rudnicki, W.: Feature Selection with Boruta Package (2010)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. arXiv1603.02754 [cs], pp. 785–794 (2016)
Kramer, O.: K-nearest neighbors. In: Dimensionality Reduction with Unsupervised Nearest Neighbors, pp. 13–23. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38652-7_2
Rifkin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004)
Scheibe, K.M., et al.: ETHOSYS (R)—new system for recording and analysis of behaviour of free-ranging domestic animals and wildlife. Appl. Anim. Behav. Sci. 55, 195–211 (1998)
Alvarenga, F.A.P., Borges, I., Palkovič, L., Rodina, J., Oddy, V.H., Dobos, R.C.: Using a three-axis accelerometer to identify and classify sheep behaviour at pasture. Appl. Anim. Behav. Sci. 181, 91–99 (2018)
Acknowledgements
The authors would like to acknowledge and thank the Douglas Bomford Trust for the financial and moral support during the project. Additionally, we thank the authors who made their dataset publicly available for use by the community [23].
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Kleanthous, N. et al. (2018). Machine Learning Techniques for Classification of Livestock Behavior. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_26
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