Machine Learning Techniques for Classification of Livestock Behavior
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.
KeywordsMachine learning Feature extraction Feature selection Animal behavior Signal processing Accelerometer Gyroscope Magnetometer
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 .
- 5.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)Google Scholar
- 14.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)Google Scholar
- 19.Le Roux, S., Wolhuter, R., Niesler, T.: An overview of automatic behaviour classification for animal-borne sensor applications in South Africa (2017)Google Scholar
- 22.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)Google Scholar
- 23.Kamminga, J.W.: Generic online animal activity recognition on collar tags (2017)Google Scholar
- 24.Mitra, S.K.: Digital Signal Processing: A Computer-Based Approach. McGraw-Hill School Education Group (2001)Google Scholar
- 25.Rabiner, L.R., Gold, B.: Theory and Application of Digital Signal Processing (1975)Google Scholar
- 27.Kursa, M.B., Rudnicki, W.: Feature Selection with Boruta Package (2010)Google Scholar
- 30.Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. arXiv1603.02754 [cs], pp. 785–794 (2016)Google Scholar