Monitoring Livestock Health by Modeling Rumination Behavior According to Accelerometer-Based Information

ماشین های کشاورزی

Article 3, Volume 15, Issue 2 - Serial Number 36, June 2025, Pages 179-192   

Document Type: Research Article

DOI: 10.22067/jam.2024.86082.1216

Authors

E. Vahedi Tekmehdash¹; H. Navid^{* 1}; H. Ghasemzadeh¹; H. Karimi²; M. Javani Holan¹

¹Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Tabriz, Tabriz, Iran

²Agricultural Engineering Research Department, Kerman Agricultural and Resource Research and Education Center, AREEO, Kerman, Iran

Abstract

Introduction
The livestock sector excels in the production of dairy and meat products. These products, serving as vital sources of animal protein, hold a significant position in household diets. The significance of these two products in the food basket has heightened awareness around animal health. Regularly tracking rumination time serves as a vital and insightful measure to obtain information about the rest and overall health of an animal. This information enables prompt intervention for health or nutritional issues, allowing for earlier management adjustments and veterinary care to effectively combat the onset of disease. In the past, rumination was usually monitored through visual observation by on-site staff or through videos recorded by cameras installed on the livestock. Nowadays, the growing scale of livestock farms makes it impractical to effectively monitor the animals individually. The traditional method of visual observation demands the continuous presence of livestock professionals and is extremely time-consuming. Currently, sensors and digital technologies have become important tools for accurate animal husbandry, enabling real-time monitoring of rumination. A review of the research in the field of precision animal husbandry shows that many efforts are being made to develop precision monitoring sensors to overcome the mentioned problems. Continuous and automatic monitoring of animal behavior through sensors can offer valuable insights into nutrition, reproduction, health, and overall well-being of dairy cows.
Materials and Methods
In this research, an accelerometer-based sensor was developed and used in the precision agriculture laboratory of Tabriz University, Iran. The sensor was installed in three different positions on the cow's body to collect data. Important factors were calculated from the raw data, and the modeling was done using the logistic regression method. The logistic regression model was trained to distinguish rumination from the other cow's behaviors. The developed model was evaluated using the receiver operating characteristic (ROC) curve, and three other evaluation criteria: precision, sensitivity, and F-score. Finally, the performance of the final model and sensor was evaluated in the field for a few days.
Results and Discussion
After calculating the evaluation criteria for different calculation factors, four optimal factors were finally selected from the 50 arrays. Muzzle mode was found to be the best place to install the sensor. Logistic regression was the best modeling method for binary classification between rumination and other behaviors. The evaluation criteria of the model in the proposed sensor are the highest, and the values of sensitivity 88%, accuracy 94%, and F-score 91% were obtained through logistic regression analysis. The final test results of the model revealed that the sensor demonstrated an impressive detection capability of 89.47%. Furthermore, the developed system exhibited strong alignment with the actual field observations, highlighting its effectiveness and reliability. Finally, the results of the current study were compared with other studies in the literature.
Conclusion
This study investigated recording and monitoring rumination behavior using an accelerometer, which can help prevent financial losses in cattle farms. After examining different mounting locations of the sensor, it was found that the muzzle position provided more accurate detections than the other mounting locations. The final model was created using the statistical factors and the calculation of the evaluation criteria. The results showed that the proposed model provided more correct diagnoses and achieved the optimal solution.
Acknowledgment
We would like to express our gratitude to the Khalat Poushan Cattle Farming Complex of the University of Tabriz, Iran, its professors and staff for supporting this project, and for their commitment to promote animal husbandry science.

Keywords

Accuracy; Classification; ROC curve; Sensitivity

References

1. Antanaitis, R., Juozaitienė, V., Malašauskienė, D., & Televičius, M. (2019). Can rumination time and some blood biochemical parameters be used as biomarkers for the diagnosis of subclinical acidosis and subclinical ketosis? Veterinary and Animal Science, 8, 100077. https://doi.org/10.1016/j.vas.2019.100077
2. Ayadi, S., Ben Said, A., Jabbar, R., Aloulou, C., Chabbouh, A., & Achballah, A. B. (2020). Dairy cow rumination detection: A deep learning approach. In Distributed Computing for Emerging Smart Networks: Second International Workshop, DiCES-N 2020, Bizerte, Tunisia, December 18, 2020, Proceedings 2 (pp. 123-139). Springer International Publishing. https://doi.org/10.1007/978-3-030-65810-6_7
3. Beauchemin, K. A. (2018). Invited review: Current perspectives on eating and rumination activity in dairy cows. Journal of Dairy Science, 101(6), 4762-4784. https://doi.org/10.3168/jds.2017-13706
4. Behneghar, H., Majidi, B., & Movaghar, A. (2021). Design of Hardware and Software Platform for Intelligent Automation of Livestock Farming using Internet of Things. Agricultural Mechanization and Systems Research, 22(78), 107-126. https://doi.org/10.22092/amsr.2021.352371.1367
5. Berckmans, D., & Guarino, M. (2017). From the Editors: Precision livestock farming for the global livestock sector. Animal Frontiers, 7(1), 4-5. https://doi.org/10.2527/af.2017.0101
6. Benaissa, S., Tuyttens, F. A., Plets, D., De Pessemier, T., Trogh, J., Tanghe, E., ... & Sonck, B. (2019). On the use of on-cow accelerometers for the classification of behaviours in dairy barns. Research in Veterinary Science, 125, 425-433. https://doi.org/10.1016/j.rvsc.2017.10.005
7. Cavaliere, A., & Ventura, V. (2018). Mismatch between food sustainability and consumer acceptance toward innovation technologies among millennial students: The case of Shelf Life Extension. Journal of Cleaner Production, 175, 641-650. https://doi.org/10.1016/j.jclepro.2017.12.087
8. Chang, A. Z., Fogarty, E. S., Moraes, L. E., García-Guerra, A., Swain, D. L., & Trotter, M. G. (2022). Detection of rumination in cattle using an accelerometer ear-tag: A comparison of analytical methods and individual animal and generic models. Computers and Electronics in Agriculture, 192, 106595. https://doi.org/10.1016/j.compag.2021.106595
9. Cocco, R., Canozzi, M. E. A., & Fischer, V. (2021). Rumination time as an early predictor of metritis and subclinical ketosis in dairy cows at the beginning of lactation: Systematic review-meta-analysis. Preventive Veterinary Medicine, 189, 105309. https://doi.org/10.1016/j.prevetmed.2021.105309
10. Eldesouky, A., Mesias, F. J., Elghannam, A., & Escribano, M. (2018). Can extensification compensate livestock greenhouse gas emissions? A study of the carbon footprint in Spanish agroforestry systems. Journal of Cleaner Production, 200, 28-38. https://doi.org/10.1016/j.jclepro.2018.07.279
11. Fournel, S., Rousseau, A. N., & Laberge, B. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosystems Engineering, 155, 96-123. https://doi.org/10.1016/j.biosystemseng.2016.12.005
12. Grinter, L. N., Campler, M. R., & Costa, J. H. C. (2019). Validation of a behavior-monitoring collar's precision and accuracy to measure rumination, feeding, and resting time of lactating dairy cows. Journal of Dairy Science, 102(4), 3487-3494. https://doi.org/10.3168/jds.2018-15563
13. Gusterer, E., Kanz, P., Krieger, S., Schweinzer, V., Süss, D., Lidauer, L., ... & Iwersen, M. (2020). Sensor technology to support herd health monitoring: Using rumination duration and activity measures as unspecific variables for the early detection of dairy cows with health deviations. Theriogenlogy, 157, 61-69. https://doi.org/10.1016/j.theriogenology.2020.07.028
14. Javani, M., Navid, H., Karimi, H., Hosseinkhani, A., & Vahedi Tekmehdash, E. (2022). Development of a method to measure cow rumination. Thesis.
15. Liboreiro, D. N., Machado, K. S., Silva, P. R., Maturana, M. M., Nishimura, T. K., Brandão, A. P., ... & Chebel, R. C. (2015). Characterization of peripartum rumination and activity of cows diagnosed with metabolic and uterine diseases. Journal of Dairy Science, 98(10), 6812-6827. https://doi.org/10.3168/jds.2014-8947
16. Meen, G. H., Schellekens, M. A., Slegers, M. H. M., Leenders, N. L. G., van Erp-van der Kooij, E., & Noldus, L. P. (2015). Sound analysis in dairy cattle vocalisation as a potential welfare monitor. Computers and Electronics in Agriculture, 118, 111-115. https://doi.org/10.1016/j.compag.2015.08.028
17. Pahl, C., Hartung, E., Mahlkow-Nerge, K., & Haeussermann, A. (2015). Feeding characteristics and rumination time of dairy cows around estrus. Journal of Dairy Science, 98(1), 148-154. https://doi.org/10.3168/jds.2014-8025
18. Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural Processes, 148, 56-62. https://doi.org/10.1016/j.beproc.2018.01.004
19. Shen, W., Zhang, A., Zhang, Y., Wei, X., & Sun, J. (2020). Rumination recognition method of dairy cows based on the change of noseband pressure. Information Processing in Agriculture, 7(4), 479-490. https://doi.org/10.1016/j.inpa.2020.01.005
20. Smith, D., Rahman, A., Bishop-Hurley, G. J., Hills, J., Shahriar, S., Henry, D., & Rawnsley, R. (2016). Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems. Computers and Electronics in Agriculture, 131, 40-50. https://doi.org/10.1016/j.compag.2016.10.006
21. Stangaferro, M. L., Wijma, R., Caixeta, L. S., Al-Abri, M. A., & Giordano, J. O. (2016). Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders. Journal of Dairy Science, 99(9), 7395-7410. https://doi.org/10.3168/jds.2016-10907
22. Syarif, I., Ahsan, A. S., Al Rasyid, M. U. H., & Pratama, Y. P. (2019, September). Health monitoring and early diseases detection on dairy cow based on internet of things and intelligent system. In 2019 International Electronics Symposium (IES) (pp. 183-188). IEEE. https://doi.org/10.1109/ELECSYM.2019.8901527
23. Topp-Becker, J., & Ellis, J. D. (2017). The role of sustainability reporting in the agri-food supply chain. Journal of Agriculture and Environmental Sciences, 6(1), 17-29. https://doi.org/10.15640/jaes.v6n1a2
24. Wang, L., Xie, Q., & Xu, Y. (2017). Recognition and analysis of ruminating behavior of dairy cows based on wearable device. Animal Environment and Welfare.
25. Zambelis, A., Wolfe, T., & Vasseur, E. (2019). Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tiestall-housed dairy cattle. Journal of Dairy Science, 102(5), 4536-4540. https://doi.org/10.3168/jds.2018-15766
26. Zehner, N., Umstätter, C., Niederhauser, J. J., & Schick, M. (2017). System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Computers and Electronics in Agriculture, 136, 31-41. https://doi.org/10.1016/j.compag.2017.02.021