Abstract
Farmers need to detect any anomaly in animals as soon as possible for production efficiency (e.g. detection of estrus) and animal welfare (e.g. detection of diseases). The number of animals per farm is however increasing, making it difficult to detect anomalies. To help solving this problem, we undertook a study on dairy cows, in which their activity was captured by an indoor tracking system and considered as time series. The state of cows (diseases, estrus, no problem) was manually labelled by animal caretakers or by a sensor for ruminal pH (acidosis). In the present study, we propose a new Fourier based method (FBAT) to detect anomalies in time series. We compare FBAT with the best machine learning methods for time series classification in the current literature (BOSS, Hive-Cote, DTW, FCN and ResNet). It follows that BOSS, FBAT and deep learning methods yield the best performance but with different characteristics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adewumi, A.O., Akinyelu, A.A.: A survey of machine-learning and nature-inspired based credit card fraud detection techniques. Int. J. Syst. Assurance Eng. Manag. 8(2), 937–953 (2017)
Bagnall, Anthony., Lines, Jason., Bostrom, Aaron., Large, James, Keogh, Eamonn: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2016). https://doi.org/10.1007/s10618-016-0483-9
Berkaya, S.K., et al.: A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, Seattle, WA, vol. 10, pp. 359–370 (1994)
Corizzo, R., Ceci, M., Japkowicz, N.: Anomaly detection and repair for accurate predictions in geo-distributed big data. Big Data Res. 16, 18–35 (2019)
Fawaz, H.I., et al.: Deep learning for time series classification: a review. Data Mining Knowl. Discov. 33(4), 917–963 (2019)
He et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Lines, J., Taylor, S., Bagnall, A.: Hive-cote: the hierarchical vote collective of transformation-based ensembles for time series classification. In: 2016 IEEE 16th International Conference on data Mining (ICDM), pp. 1041–1046. IEEE (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Marwah, R., Cawkwell, F., Hennessy, D., Green, S.: Improved estimation of grassland biomass using machine learning and satellite data. In: 9th ECPLF 2019, pp. 174–179. Teagasc (2019)
Mollenhors, H., de Haan, M., Oenema, J., Hoving-Bolink, A., Veerkamp, R., Kamphuis, C.: Machine learning to realize phosphate equilibrium at field level in dairy farming. In: 9th ECPLF 2019, pp. 41–44. Teagasc (2019)
Munir, M., Siddiqui, S.A., Dengel, A., Ahmed, S.: Deepant: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991–2005 (2018)
Ruiz, E.V., Nolla, F.C., Segovia, H.R.: Is the DTW “distance” really a metric? An algorithm reducing the number of DTW comparisons in isolated word recognition. Speech Commun. 4(4), 333–344 (1985)
Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505–1530 (2015)
Veissier, I., Mialon, M.M., Sloth, K.H.: Early modification of the circadian organization of cow activity in relation to disease or estrus. J. Dairy Sci. 100(5), 3969–3974 (2017)
Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3–11 (2019)
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578–1585. IEEE (2017)
Yang, Q., Wu, X.: 10 challenging problems in data mining research. Int. J. Inf. Technol. Decision Making 5(04), 597–604 (2006)
Acknowledgment
This collaborative work was made possible thanks to the French Government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20–25). The PhD grant for N. Wagner was provided by INRA and Université Clermont Auvergne. We thank the HERBIPOLE staff, B. Meunier, Y. Gaudron and M. Silberberg for data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wagner, N., Antoine, V., Koko, J., Mialon, MM., Lardy, R., Veissier, I. (2020). Comparison of Machine Learning Methods to Detect Anomalies in the Activity of Dairy Cows. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-59491-6_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59490-9
Online ISBN: 978-3-030-59491-6
eBook Packages: Computer ScienceComputer Science (R0)