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Do Cows Have Fingerprints? Using Time Series Techniques and Milk Flow Profiles to Characterise Cow Milking Performance and Detect Health Issues

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Advanced Analytics and Learning on Temporal Data (AALTD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14343))

Abstract

On modern dairy farms technologies that are capable of measuring high frequency indicators (e.g. milk yield, milk flow-rates, and electrical conductivity) at every milking can play an important role in helping farmers manage animal health. The most modern dairy farms use milk meters that provide detailed, high-frequency data about the flow of milk during every milking (cows are typically milked twice daily). This forms a time series that we call a milk flow profile. As cows are milked twice per day, every day this data forms a series of time series collected in a relatively controlled way that offers detailed insights into cow milking performance and cow health. In this paper we show that milk flow profiles act as a finger print for cows in a herd and offer opportunities for extracting useful insights about cow health that are unexplored. We demonstrate that unsupervised time series clustering approaches, particularly those that utilize the shape of time series, can be used to characterize a herd and that supervised approaches applied to milk flow profiles can be used for automated mastitis detection. In the latter case it is interesting that approaches using standard machine learning methods applied to features extracted from milk flow profiles, out-perform approaches specifically designed for time series.

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Notes

  1. 1.

    This is typically known as milk recording data and includes information such as the percentage of fat and lactose contained in the milk.

  2. 2.

    A milk meter is a device inserted into the milk pipeline that records the individual animal milk yield during milking.

  3. 3.

    A midi-line 30 unit Dairymaster herringbone, swing-over milking system (Dairymaster, Ireland) was used to milk the cows twice per day. The milking system utilised simultaneous pulsation and was fitted with automatic cluster removers and weigh-all milk meters (Dairymaster, Ireland). The standard farm milk flow rate switch-point of the automatic cluster removers was 0.2 kg/min.

  4. 4.

    The majority of cows were from the Holstein-Friesian breed. 92 of the cows were at parity of 1 (meaning they had given birth to just one calf, or were primiparous) and the remaining 201 were at parity of 2 or greater (meaning they had given birth to just one calf, or were multiparous).

  5. 5.

    Milk composition analysis, often know as milk recording, is used to analyse the content of milk. Typically the percentage of fat, protein, and lactose in the milk is measured as well as the amount of casein and urea (both important in cheese making) present. The number of somatic cells, usually white blood cells, is also measured and typically referred to as somatic cell count (SCC). SCC is the most used and studied indicator in mastitis detection research, especially for sub-clinical mastitis detection [36]. A Fossomatic machine (Foss, Denmark) was used to measure SCC and other indicators of milk composition.

  6. 6.

    All features were extracted from flow profile data using the tsfresh package in Python [5].

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Acknowledgements

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland under Grant Number [16/RC/3835]. Further financial and technical support was provided by Dairymaster.

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Authors and Affiliations

  1. School of Computer Science, University College Dublin, Dublin, Ireland

    Changhong Jin & Brian Mac Namee

  2. VistaMilk SFI Research Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland

    Changhong Jin, John Upton & Brian Mac Namee

  3. Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland

    John Upton

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  1. Changhong Jin
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Correspondence to Changhong Jin .

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Editors and Affiliations

  1. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

  2. University of Rennes 2, Rennes, France

    Romain Tavenard

  3. University of Southampton, Southampton, UK

    Anthony Bagnall

  4. Humboldt University of Berlin, Berlin, Germany

    Patrick Schaefer

  5. University of Rennes, Rennes, France

    Simon Malinowski

  6. Claude Bernard University Lyon 1, Villeurbanne, France

    Thomas Guyet

  7. Orange Innovation, Lannion, France

    Vincent Lemaire

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Jin, C., Upton, J., Mac Namee, B. (2023). Do Cows Have Fingerprints? Using Time Series Techniques and Milk Flow Profiles to Characterise Cow Milking Performance and Detect Health Issues. In: Ifrim, G., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2023. Lecture Notes in Computer Science(), vol 14343. Springer, Cham. https://doi.org/10.1007/978-3-031-49896-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-49896-1_15

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