Browse > Article
http://dx.doi.org/10.6109/jkiice.2018.22.11.1421

Health Monitoring of Livestock using Neck Sensor based on Machine Learning  

Lee, Woongsup (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Park, Seongmin (Dairy Science Division, National Institute of Animal science, Rural Development Administration)
Ban, Tae-Won (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Kim, Seong Hwan (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Ryu, Jongyeol (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Sung, Kil-Young (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.11, 2018 , pp. 1421-1427 More about this Journal
Abstract
Due to the rapid development of Internet-of-Things technology, different types of smart sensors are now devised and deployed widely. These smart sensors are now used in animal husbandry which was traditionally managed by the experience of farmers, such that wearable sensors for livestock, and the smart farm which is equipped with multiple sensors are utilized to increase the efficiency of livestock management. Herein, we consider a scheme in which the body temperature and the level of activity are measured by smart sensor which is attached to the neck of dairy cattle and the health condition is monitored based on collected data. Especially, we find that the estrous of dairy cattle which is one of most important metric in milk production, can be predicted with high precision using various machine learning techniques. By utilizing the proposed prediction scheme, estrous of cattle can be detected immediately and this can improve the efficiency of cattle management.
Keywords
Dairy cattle; Health monitoring; Machine learning; Wearable sensor;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 A. Whitmore, A. Agarwal, and L. Da Xu, "The Internet of Things-A Survey of Topics and Trends," Information Systems Frontiers, vol. 17, no. 2, pp. 261-274, Apr. 2015.   DOI
2 W. Lee, S. Kim, J. Ryu, and T. Ban, "Fast Detection of Disease in Livestock based on Deep Learning," Journal of the Korea Institute of Information and Communication Engineering, vol. 21, no. 5, pp. 1009-1015, May 2017.   DOI
3 M. A. Kashiha, C. Bahr, S. Ott, C. P. Moons, T. A. Niewold, F. Tuyttens, and D. Berckmans, "Automatic Monitoring of Pig Locomotion Using Image Analysis," Livestock Science, vol. 159, no. 1, pp. 141-148, Jan. 2014.   DOI
4 S. J. Roberts, R. Cain, and M. S. Dawkins, "Prediction of Welfare Outcomes For Broiler Chickens Using Bayesian Regression on Continuous Optical Flow Data," Journal of the Royal Society interface, vol. 9, no. 77, pp. 3436-3443, Sep. 2012.   DOI
5 I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques. 4th ed. Burlington, MI: Morgan Kaufmann, 2016.
6 K. Han, W. Lee, and K. Sung, "Development of a Model to Analyze The Relationship Between Smart Pig-farm Environmental Data and Daily Weight Increase Based on Decision Tree," Journal of Korea Institute of Information and Communication Engineering, vol. 20, no. 12, pp. 2348-2354, Dec. 2016.   DOI
7 W. Lee, J. Ryu, T. Ban, S. Kim, and H. Choi, "Prediction of Water Usage in Pig Farm based on Machine Learning," Journal of Korea Institute of Information and Communication Engineering, vol. 21, no. 8, pp. 1560-1566, Aug. 2017.   DOI
8 A. Madureira, B. Silper, T. Burnett, L. Polsky, L. Cruppe, D. Veira, J. Vasconcelos, and R. Cerri, "Factors Affecting Expression of Estrus Measured by Activity Monitors and Conception Risk of Lactating Dairy Cows," Journal of Dairy Science, vol. 98, no. 10, pp. 7003-7014, Oct. 2015.   DOI
9 Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, May. 2015.   DOI