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Optimized Deep-Learning-Based Method for Cattle Udder Traits Classification

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  • Hina Afridi

    (Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
    Geno SA, Storhamargata 44, 2317 Hamar, Norway)

  • Mohib Ullah

    (Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway)

  • Øyvind Nordbø

    (Geno SA, Storhamargata 44, 2317 Hamar, Norway
    Norsvin SA, Storhamargata 44, 2317 Hamar, Norway)

  • Faouzi Alaya Cheikh

    (Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway)

  • Anne Guro Larsgard

    (Geno SA, Storhamargata 44, 2317 Hamar, Norway)

Abstract

We propose optimized deep learning (DL) models for automatic analysis of udder conformation traits of cattle. One of the traits is represented by supernumerary teats that is in excess of the normal number of teats. Supernumerary teats are the most common congenital heritable in cattle. Therefore, the major advantage of our proposed method is its capability to automatically select the relevant images and thereafter perform supernumerary teat classification when limited data are available. For this purpose, we perform experimental analysis on the image dataset that we collected using a handheld device consisting of a combined depth and RGB camera. To disclose the underlying characteristics of our data, we consider the uniform manifold approximation and projection (UMAP) technique. Furthermore, for comprehensive evaluation, we explore the impact of different data augmentation techniques on the performances of DL models. We also explore the impact of only RGB data and the combination of RGB and depth data on the performances of the DL models. For this purpose, we integrate the three channels of RGB data with the depth channel to generate four channels of data. We present the results of all the models in terms of four performance metrics, namely accuracy, F-score, precision, and sensitivity. The experimental results reveal that a higher level of data augmentation techniques improves the performances of the DL models by approximately 10%. Our proposed method also outperforms the reference methods recently introduced in the literature.

Suggested Citation

  • Hina Afridi & Mohib Ullah & Øyvind Nordbø & Faouzi Alaya Cheikh & Anne Guro Larsgard, 2022. "Optimized Deep-Learning-Based Method for Cattle Udder Traits Classification," Mathematics, MDPI, vol. 10(17), pages 1-19, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3097-:d:900359
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    References listed on IDEAS

    as
    1. Snezhana Gocheva-Ilieva & Antoaneta Yordanova & Hristina Kulina, 2022. "Predicting the 305-Day Milk Yield of Holstein-Friesian Cows Depending on the Conformation Traits and Farm Using Simplified Selective Ensembles," Mathematics, MDPI, vol. 10(8), pages 1-20, April.
    2. Leishi Wang & Mingtao Li & Xin Pei & Juan Zhang, 2022. "Optimal Breeding Strategy for Livestock with a Dynamic Price," Mathematics, MDPI, vol. 10(10), pages 1-24, May.
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