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Live Weight Prediction of Cattle Based on Deep Regression of RGB-D Images

Author

Listed:
  • Alexey Ruchay

    (Federal Research Centre of Biological Systems and Agro-Technologies of the Russian Academy of Sciences, 460000 Orenburg, Russia
    Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia)

  • Vitaly Kober

    (Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
    Center of Scientific Research and Higher Education of Ensenada, Ensenada 22860, Mexico)

  • Konstantin Dorofeev

    (Federal Research Centre of Biological Systems and Agro-Technologies of the Russian Academy of Sciences, 460000 Orenburg, Russia)

  • Vladimir Kolpakov

    (Federal Research Centre of Biological Systems and Agro-Technologies of the Russian Academy of Sciences, 460000 Orenburg, Russia
    Department of Biotechnology of Animal Raw Materials and Aquaculture, Orenburg State University, 460000 Orenburg, Russia)

  • Alexey Gladkov

    (Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia)

  • Hao Guo

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

Abstract

Predicting the live weight of cattle helps us monitor the health of animals, conduct genetic selection, and determine the optimal timing of slaughter. On large farms, accurate and expensive industrial scales are used to measure live weight. However, a promising alternative is to estimate live weight using morphometric measurements of livestock and then apply regression equations relating such measurements to live weight. Manual measurements on animals using a tape measure are time-consuming and stressful for the animals. Therefore, computer vision technologies are now increasingly used for non-contact morphometric measurements. The paper proposes a new model for predicting live weight based on augmenting three-dimensional clouds in the form of flat projections and image regression with deep learning. It is shown that on real datasets, the accuracy of weight measurement using the proposed model reaches 91.6%. We also discuss the potential applicability of the proposed approach to animal husbandry.

Suggested Citation

  • Alexey Ruchay & Vitaly Kober & Konstantin Dorofeev & Vladimir Kolpakov & Alexey Gladkov & Hao Guo, 2022. "Live Weight Prediction of Cattle Based on Deep Regression of RGB-D Images," Agriculture, MDPI, vol. 12(11), pages 1-17, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1794-:d:956531
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    References listed on IDEAS

    as
    1. Yihu Hu & Xinying Luo & Zicheng Gao & Ao Du & Hao Guo & Alexey Ruchay & Francesco Marinello & Andrea Pezzuolo, 2022. "Curve Skeleton Extraction from Incomplete Point Clouds of Livestock and Its Application in Posture Evaluation," Agriculture, MDPI, vol. 12(7), pages 1-19, July.
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    Cited by:

    1. Myung Hwan Na & Wanhyun Cho & Sora Kang & Inseop Na, 2023. "Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth," Agriculture, MDPI, vol. 13(10), pages 1-15, September.

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