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Dairy Cow Rumination Detection: A Deep Learning Approach

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Distributed Computing for Emerging Smart Networks (DiCES-N 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1348))

Abstract

Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Therefore, various monitoring methods and measurement equipment have been used to assess cattle behavior. However, these modern attached devices are invasive, stressful and uncomfortable for the cattle and can influence negatively the welfare and diurnal behavior of the animal. Multiple research efforts addressed the problem of rumination detection by adopting new methods by relying on visual features. However, they only use few postures of the dairy cow to recognize the rumination or feeding behavior. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models. The classification process is conducted under two main labels: ruminating and other, using all cow postures captured by the monitoring camera. Our proposed system is simple and easy-to-use which is able to capture long-term dynamics using a compacted representation of a video in a single 2D image. This method proved efficiency in recognizing the rumination behavior with 95%, 98% and 98% of average accuracy, recall and precision, respectively.

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Notes

  1. 1.

    https://www.rumiwatch.ch/.

  2. 2.

    https://www.crunchbase.com/organization/lifeye.

  3. 3.

    https://www.moome.io.

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Acknowledgment

This research work is supported by LifeEye LLC. The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. LifeEye LLC, Tunis, Tunisia

    Safa Ayadi, Ahmed Ben Said, Rateb Jabbar, Achraf Chabbouh & Ahmed Ben Achballah

  2. Faculty of Economics and Management of Sfax, University of Sfax, 3018, Sfax, Tunisia

    Safa Ayadi & Chafik Aloulou

  3. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Ahmed Ben Said & Rateb Jabbar

Authors
  1. Safa Ayadi
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  2. Ahmed Ben Said
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  6. Ahmed Ben Achballah
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Correspondence to Safa Ayadi .

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Editors and Affiliations

  1. University of Carthage, Zarzouna Bizerte, Tunisia

    Imen Jemili

  2. Bordeaux INP, France Talence, France

    Mohamed Mosbah

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Ayadi, S., Ben Said, A., Jabbar, R., Aloulou, C., Chabbouh, A., Achballah, A.B. (2020). Dairy Cow Rumination Detection: A Deep Learning Approach. In: Jemili, I., Mosbah, M. (eds) Distributed Computing for Emerging Smart Networks. DiCES-N 2020. Communications in Computer and Information Science, vol 1348. Springer, Cham. https://doi.org/10.1007/978-3-030-65810-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-65810-6_7

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