Ir al contenido

Documat

Evaluation of One-Class Techniques for Early Estrus Detection on Galician Intensive Dairy Cow Farm Based on Behavioral Data From Activity Collars

  • Michelena, Álvaro [1] ; Jove, Esteban [1] Árbol académico ; Fontenla-Romero, Óscar [1] Árbol académico ; Calvo-Rolle, José-Luis [1] Árbol académico
    1. [1] Universidade da Coruña

      Universidade da Coruña

      A Coruña, España

  • Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 13, Nº. 1, 2024
  • Idioma: inglés
  • DOI: 10.14201/adcaij.32508
  • Enlaces
  • Resumen

    • 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.

  • Referencias bibliográficas
    • Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. S. (2019). Effects...
    • Alonso, M. E., Gonzalez-Montana, J. R., & Lomillos, J. M. (2020). Consumers’ concerns and perceptions of farm animal welfare. Animals,...
    • Al-Qudah, M., Ashi, Z., Alnabhan, M., & Abu Al-Haija, Q. (2023). Effective one-class classifier model for memory dump malware detection....
    • Annas, M., & Wahab, S. N. (2023). Data mining methods: K-means clustering algorithms. International Journal of Cyber and IT Service Management,...
    • Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of k-nearest neighbor genetic, support vector machine, decision...
    • Bruyere, P., Hetreau, T., Ponsart, C., Gatien, J., Buff, S., Disenhaus, C., Giroud, O., & Guerin, P.(2012). Can video cameras replace...
    • Carvajal, A., Martinez, E., Tapia, M., & Ayke, I. T. (2020). El ciclo estral en la hembra bovina y su importancia productiva. Instituto...
    • Casale, P., Pujol, O., & Radeva, P. (2011). Approximate convex hulls family for one-class classification. Multiple Classifier Systems:...
    • Cocco, R., Canozzi, M. E. A., & Fischer, V. (2021). Rumination time as an early predictor of metritis and subclinical ketosis in dairy...
    • Fernandez-Francos, D., Fontenla-Romero, O., & Alonso-Betanzos, A. (2017). One-class convex hull-based algorithm for classification in...
    • Fesseha, H., & Degu, T. (2020). Estrus detection, estrus synchronization in cattle and it’s economic importance. Int. J. Vet. Res, 3(1),...
    • Fogsgaard, K. K., Bennedsgaard, T. W., & Herskin, M. S. (2015). Behavioral changes in freestall-housed dairy cows with naturally occurring...
    • 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...
    • 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...
    • Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews...
    • Hu, H., Liu, J., Zhang, X., & Fang, M. (2023). An effective and adaptable k-means algorithm for big data cluster analysis. Pattern Recognition,...
    • Jabbar, W. A., Subramaniam, T., Ong, A. E., Shu’Ib, M. I., Wu, W., & De Oliveira, M. A. (2022). Lorawan-based iot system implementation...
    • Jove, E., Casteleiro-Roca, J.-L., Quintian, H., Mendez-Perez, J.-A., & Calvo-Rolle, J. L. (2021). A new method for anomaly detection based...
    • Juszczak, P., Tax, D. M., Pe, kalska, E., & Duin, R. P. (2009). Minimum spanning tree based one-class classifier [Advances in Machine...
    • La Grassa, R., Gallo, I., & Landro, N. (2022). Ocmst: One-class novelty detection using convolutional neural network and minimum spanning...
    • 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...
    • Michelena, A., Fontenla-Romero, O., & Luis Calvo-Rolle, J. (2024). A review and future trends of precision livestock over dairy and beef...
    • Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and precisión livestock farming (plf): An up to date overview...
    • Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29,...
    • Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M.-C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021)....
    • Pratama, Y. P., Basuki, D. K., Sukaridhoto, S., Yusuf, A. A., Yulianus, H., Faruq, F., & Putra, F. B. (2019). Designing of a smart collar...
    • 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...
    • Riaz, U., Idris, M., Ahmed, M., Ali, F., & Yang, L. (2023). Infrared thermography as a potential non-invasive tool for estrus detection...
    • Roelofs, J., Van Eerdenburg, F., Soede, N., & Kemp, B. (2005). Various behavioral signs of estrous and their relationship with time of...
    • Roelofs, J., Lopez-Gatius, F., Hunter, R., Van Eerdenburg, F., & Hanzen, C. (2010). When is a cow in estrus? clinical and practical aspects....
    • 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,...
    • 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...
    • 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...
    • Thornton, P. K. (2010). Livestock production: Recent trends, future prospects. Philosophical Transactions of the Royal Society B: Biological...
    • Wang, J., Bell, M., Liu, X., & Liu, G. (2020). Machine-learning techniques can enhance dairy cow estrus detection using location and acceleration...
    • 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,...
Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno