Evaluation of One-Class Techniques for Early Estrus Detection on Galician Intensive Dairy Cow Farm Based on Behavioral Data From Activity Collars
Abstract Nowadays, precision livestock farming has revolutionized the livestock industry by providing it with devices and tools that significantly improve farm management. Among these technologies, smart collars have become a very common device due to their ability to register individual cow behavior in real time. These data provide the opportunity to identify behavioral patterns that can be analyzed to detect relevant conditions, such as estrus. Against this backdrop, this research work evaluates and compares the effectiveness of six one-class techniques for estrus early detection in dairy cows in intensive farms based on data collected by a commercial smart collar. For this research, the behavior of 10 dairy cows from a cattle farm in Spain was monitored. Feature engineering techniques were applied to the data obtained by the collar, in order to add new variables and enhance the dataset. Some techniques achieved F1-Score values exceeding 95 % in certain cows. However, considerable variability in the results was observed among different animals, highlighting the need to develop individualized models for each cow. In addition, the results suggest that incorporating a temporal context of the animal’s previous behavior is key to improving model performance. Specifically, it was found that when considering a period of 8 hours prior, the performance of the evaluated techniques was substantially improved.
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Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. S. (2019). Effects of distance measure choice on k-nearest neighbor classifier performance: A review. Big data, 7(4), 221–248.
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Hu, H., Liu, J., Zhang, X., & Fang, M. (2023). An effective and adaptable k-means algorithm for big data cluster analysis. Pattern Recognition, 139, 109404.
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Jove, E., Casteleiro-Roca, J.-L., Quintian, H., Mendez-Perez, J.-A., & Calvo-Rolle, J. L. (2021). A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections. Information Fusion, 65, 50–57.
Juszczak, P., Tax, D. M., Pe, kalska, E., & Duin, R. P. (2009). Minimum spanning tree based one-class classifier [Advances in Machine Learning and Computational Intelligence]. Neurocomputing, 72(7), 1859–1869. 10.1016/j.neucom.2008.05.003
La Grassa, R., Gallo, I., & Landro, N. (2022). Ocmst: One-class novelty detection using convolutional neural network and minimum spanning trees. Pattern Recognition Letters, 155, 114–120.
Ma, N., Pan, L., Chen, S., & Liu, B. (2020). Nb-iot estrus detection system of dairy cows based on lstm networks. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 1–5.
Michelena, A., Fontenla-Romero, O., & Luis Calvo-Rolle, J. (2024). A review and future trends of precision livestock over dairy and beef cow cattle with artificial intelligence. Logic Journal of the IGPL, jzae111. 10.1093/jigpal/jzae111
Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and precisión livestock farming (plf): An up to date overview across animal productions. Sensors, 22(12), 4319.
Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367.
Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M.-C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, 106406.
Pratama, Y. P., Basuki, D. K., Sukaridhoto, S., Yusuf, A. A., Yulianus, H., Faruq, F., & Putra, F. B. (2019). Designing of a smart collar for dairy cow behavior monitoring with application monitoring in microservices and internet of things-based systems. 2019 International Electronics Symposium (IES), 527–533.
Raschka, S. (2014). An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330.
Reith, S., & Hoy, S. (2018). Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle. Animal, 12(2), 398–407.
Riaz, U., Idris, M., Ahmed, M., Ali, F., & Yang, L. (2023). Infrared thermography as a potential non-invasive tool for estrus detection in cattle and buffaloes. Animals, 13(8), 1425.
Roelofs, J., Van Eerdenburg, F., Soede, N., & Kemp, B. (2005). Various behavioral signs of estrous and their relationship with time of ovulation in dairy cattle. Theriogenology, 63(5), 1366–1377.
Roelofs, J., Lopez-Gatius, F., Hunter, R., Van Eerdenburg, F., & Hanzen, C. (2010). When is a cow in estrus? clinical and practical aspects. Theriogenology, 74(3), 327–344.
Ruviaro, C. F., de Leis, C. M., Florindo, T. J., de Medeiros Florindo, G. I. B., da Costa, J. S.,Tang, W. Z., Pinto, A. T., & Soares, S. R. (2020). Life cycle cost analysis of dairy production systems in southern Brazil. Science of the Total Environment, 741, 140273.
Scott, D. W. (2015). Multivariate density estimation: Theory, practice, and visualization. John Wiley & Sons.
Silper, B., Madureira, A., Kaur, M., Burnett, T., & Cerri, R. (2015). Comparison of estrus characteristics in holstein heifers by 2 activity monitoring systems. Journal of dairy science, 98(5), 3158–3165.
Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised k-means clustering algorithm. IEEE access, 8, 80716–80727.
Tax, D. M. J. (2001). One-class classification: Concept-learning in the absence of counter-examples [ph. d. thesis]. Delft University of Technology.
Tax, D. (2018, January). Ddtools, the data description toolbox for matlab [version 2.1.3].
Thanh, L. T., Nishikawa, R., Takemoto, M., Binh, H. T. T., & Nakajo, H. (2018). Cow estrus detection via discrete wavelet transformation and unsupervised clustering. Proceedings of the 9th International Symposium on Information and Communication Technology, 305–312.
Thornton, P. K. (2010). Livestock production: Recent trends, future prospects. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 2853–2867. 10.1098/rstb.2010.0134
Wang, J., Bell, M., Liu, X., & Liu, G. (2020). Machine-learning techniques can enhance dairy cow estrus detection using location and acceleration data. Animals, 10(7), 1160.
Zhang, X., & Liu, C.-A. (2023). Model averaging prediction by k-fold cross-validation. Journal of Econometrics, 235(1), 280–301.
Zhang, Z. (2016). Introduction to machine learning: K-nearest neighbors. Annals of translational medicine, 4(11).
Zheng, A., & Casari, A. (2018). Feature engineering for machine learning: Principles and techniques for data scientists. O’Reilly Media, Inc.
Alonso, M. E., Gonzalez-Montana, J. R., & Lomillos, J. M. (2020). Consumers’ concerns and perceptions of farm animal welfare. Animals, 10(3), 385.
Al-Qudah, M., Ashi, Z., Alnabhan, M., & Abu Al-Haija, Q. (2023). Effective one-class classifier model for memory dump malware detection. Journal of Sensor and Actuator Networks, 12(1), 5.
Annas, M., & Wahab, S. N. (2023). Data mining methods: K-means clustering algorithms. International Journal of Cyber and IT Service Management, 3(1), 40–47.
Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of k-nearest neighbor genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, 100071.
Bruyere, P., Hetreau, T., Ponsart, C., Gatien, J., Buff, S., Disenhaus, C., Giroud, O., & Guerin, P.(2012). Can video cameras replace visual estrus detection in dairy cows? Theriogenology, 77(3), 525–530.
Carvajal, A., Martinez, E., Tapia, M., & Ayke, I. T. (2020). El ciclo estral en la hembra bovina y su importancia productiva. Instituto de investigaciones agropecuarias, 246, 1–4.
Casale, P., Pujol, O., & Radeva, P. (2011). Approximate convex hulls family for one-class classification. Multiple Classifier Systems: 10th International Workshop, MCS 2011,
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.
Fernandez-Francos, D., Fontenla-Romero, O., & Alonso-Betanzos, A. (2017). One-class convex hull-based algorithm for classification in distributed environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(2), 386–396.
Fesseha, H., & Degu, T. (2020). Estrus detection, estrus synchronization in cattle and it’s economic importance. Int. J. Vet. Res, 3(1), 1001.
Fogsgaard, K. K., Bennedsgaard, T. W., & Herskin, M. S. (2015). Behavioral changes in freestall-housed dairy cows with naturally occurring clinical mastitis. Journal of dairy science, 98(3), 1730–1738.
Fukase, E., & Martin, W. (2020). Economic growth, convergence, and world food demand and supply. World Development, 132, 104954. 10.1016/j.worlddev.2020.104954
Garcia, R., Aguilar, J., Toro, M., Pinto, A., & Rodriguez, P. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture, 179, 105826.
Gautam, G. (2023). Postpartum anestrus in dairy cattle and its management. AIP Conference Proceedings, 2628(1).
Rayas-Amor, Adolfo & Espinoza, A. & Arriaga-Jordán, Carlos & Mould, F. & Castelan-Ortega, Octavio. (2005). Grassland: a global resource. 10.3920/978-90-8686-551-2
Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100.
Hu, H., Liu, J., Zhang, X., & Fang, M. (2023). An effective and adaptable k-means algorithm for big data cluster analysis. Pattern Recognition, 139, 109404.
Jabbar, W. A., Subramaniam, T., Ong, A. E., Shu’Ib, M. I., Wu, W., & De Oliveira, M. A. (2022). Lorawan-based iot system implementation for long-range outdoor air quality monitoring. Internet of Things, 19, 100540.
Jove, E., Casteleiro-Roca, J.-L., Quintian, H., Mendez-Perez, J.-A., & Calvo-Rolle, J. L. (2021). A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections. Information Fusion, 65, 50–57.
Juszczak, P., Tax, D. M., Pe, kalska, E., & Duin, R. P. (2009). Minimum spanning tree based one-class classifier [Advances in Machine Learning and Computational Intelligence]. Neurocomputing, 72(7), 1859–1869. 10.1016/j.neucom.2008.05.003
La Grassa, R., Gallo, I., & Landro, N. (2022). Ocmst: One-class novelty detection using convolutional neural network and minimum spanning trees. Pattern Recognition Letters, 155, 114–120.
Ma, N., Pan, L., Chen, S., & Liu, B. (2020). Nb-iot estrus detection system of dairy cows based on lstm networks. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 1–5.
Michelena, A., Fontenla-Romero, O., & Luis Calvo-Rolle, J. (2024). A review and future trends of precision livestock over dairy and beef cow cattle with artificial intelligence. Logic Journal of the IGPL, jzae111. 10.1093/jigpal/jzae111
Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and precisión livestock farming (plf): An up to date overview across animal productions. Sensors, 22(12), 4319.
Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367.
Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M.-C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, 106406.
Pratama, Y. P., Basuki, D. K., Sukaridhoto, S., Yusuf, A. A., Yulianus, H., Faruq, F., & Putra, F. B. (2019). Designing of a smart collar for dairy cow behavior monitoring with application monitoring in microservices and internet of things-based systems. 2019 International Electronics Symposium (IES), 527–533.
Raschka, S. (2014). An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330.
Reith, S., & Hoy, S. (2018). Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle. Animal, 12(2), 398–407.
Riaz, U., Idris, M., Ahmed, M., Ali, F., & Yang, L. (2023). Infrared thermography as a potential non-invasive tool for estrus detection in cattle and buffaloes. Animals, 13(8), 1425.
Roelofs, J., Van Eerdenburg, F., Soede, N., & Kemp, B. (2005). Various behavioral signs of estrous and their relationship with time of ovulation in dairy cattle. Theriogenology, 63(5), 1366–1377.
Roelofs, J., Lopez-Gatius, F., Hunter, R., Van Eerdenburg, F., & Hanzen, C. (2010). When is a cow in estrus? clinical and practical aspects. Theriogenology, 74(3), 327–344.
Ruviaro, C. F., de Leis, C. M., Florindo, T. J., de Medeiros Florindo, G. I. B., da Costa, J. S.,Tang, W. Z., Pinto, A. T., & Soares, S. R. (2020). Life cycle cost analysis of dairy production systems in southern Brazil. Science of the Total Environment, 741, 140273.
Scott, D. W. (2015). Multivariate density estimation: Theory, practice, and visualization. John Wiley & Sons.
Silper, B., Madureira, A., Kaur, M., Burnett, T., & Cerri, R. (2015). Comparison of estrus characteristics in holstein heifers by 2 activity monitoring systems. Journal of dairy science, 98(5), 3158–3165.
Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised k-means clustering algorithm. IEEE access, 8, 80716–80727.
Tax, D. M. J. (2001). One-class classification: Concept-learning in the absence of counter-examples [ph. d. thesis]. Delft University of Technology.
Tax, D. (2018, January). Ddtools, the data description toolbox for matlab [version 2.1.3].
Thanh, L. T., Nishikawa, R., Takemoto, M., Binh, H. T. T., & Nakajo, H. (2018). Cow estrus detection via discrete wavelet transformation and unsupervised clustering. Proceedings of the 9th International Symposium on Information and Communication Technology, 305–312.
Thornton, P. K. (2010). Livestock production: Recent trends, future prospects. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 2853–2867. 10.1098/rstb.2010.0134
Wang, J., Bell, M., Liu, X., & Liu, G. (2020). Machine-learning techniques can enhance dairy cow estrus detection using location and acceleration data. Animals, 10(7), 1160.
Zhang, X., & Liu, C.-A. (2023). Model averaging prediction by k-fold cross-validation. Journal of Econometrics, 235(1), 280–301.
Zhang, Z. (2016). Introduction to machine learning: K-nearest neighbors. Annals of translational medicine, 4(11).
Zheng, A., & Casari, A. (2018). Feature engineering for machine learning: Principles and techniques for data scientists. O’Reilly Media, Inc.
Michelena, Álvaro, Jove, E., Fontenla-Romero, Óscar, & Calvo-Rolle, J.-L. (2024). Evaluation of One-Class Techniques for Early Estrus Detection on Galician Intensive Dairy Cow Farm Based on Behavioral Data From Activity Collars. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e32508. https://doi.org/10.14201/adcaij.32508
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