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Technology of Automatic Evaluation of Dairy Herd Fatness

Author

Listed:
  • Sergey S. Yurochka

    (Federal State Budgetary Scientific Institution, Federal Scientific Agroengineering Center VIM (FSAC VIM), 1st Institutsky Proezd 5, 109428 Moscow, Russia)

  • Igor M. Dovlatov

    (Federal State Budgetary Scientific Institution, Federal Scientific Agroengineering Center VIM (FSAC VIM), 1st Institutsky Proezd 5, 109428 Moscow, Russia)

  • Dmitriy Y. Pavkin

    (Federal State Budgetary Scientific Institution, Federal Scientific Agroengineering Center VIM (FSAC VIM), 1st Institutsky Proezd 5, 109428 Moscow, Russia)

  • Vladimir A. Panchenko

    (Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia)

  • Aleksandr A. Smirnov

    (Federal State Budgetary Scientific Institution, Federal Scientific Agroengineering Center VIM (FSAC VIM), 1st Institutsky Proezd 5, 109428 Moscow, Russia)

  • Yuri A. Proshkin

    (Federal State Budgetary Scientific Institution, Federal Scientific Agroengineering Center VIM (FSAC VIM), 1st Institutsky Proezd 5, 109428 Moscow, Russia)

  • Igor Yudaev

    (Energy Department, Kuban State Agrarian University, 350044 Krasnodar, Russia)

Abstract

The global recent development trend in dairy farming emphasizes the automation and robotization of milk production. The rapid development rate of dairy farming requires new technologies to increase the economic efficiency and improve production. The research goal was to increase the milk production efficiency by introducing the technology to automatically assess the fatness of a dairy herd in 0.25-point step on a 5-point scale. Experimental data were collected on the 3D ToF camera O3D 303 installed in a walk-through machine on robotic free-stall farms in the period from August 2020 to November 2022. The authors collected data on 182 animals and processed 546 images. All animals were between 450 and 700 kg in weight. Based on the regression analysis, they developed software to find and identify the main five regions of interest: the spinous processes of the lumbar spine and back; the transverse processes of the lumbar spine and the gluteal fossa area; the malar and sciatic tuberosities; the tail base; and the vulva and anus region. The adequacy of the proposed technology was verified by means of a parallel expert survey. The developed technology was tested on 3 farms with a total of 1810 cows and is helpful for the non-contact evaluation of the fatness of a dairy herd within the herd’s life cycle. The developed method can be used to evaluate the tail base area with 100% accuracy. The hungry hole can be determined with a 98.9% probability; the vulva and anus area—with a 95.10% probability. Protruding vertebrae—namely, spinous processes and transverse processes—were evaluated with a 52.20% and 51.10% probability. The system’s overall accuracy was assessed as 93.4%, which was a positive result. Animals in the condition of 2.5 to 3.5 at 5–6 months were considered healthy. The developed system makes it possible to divide the animals into three groups, confirming their physiological status: normal range body condition, exhaustion, and obesity. By means of a correlation dependence equal to R = 0.849 (Pearson method), the authors revealed that animals of the same breed and in the same lactation range have a linear dependence of weight-to-fatness score. They have developed an algorithm for automated assessment of the fatness of animals with further staging of their physiological state. The economic effect of implementing the proposed system has been demonstrated. The effect of increasing the production efficiency of a dairy farm by introducing the technology of automatic evaluation of the fatness of a dairy herd with a 0.25-point step on a 5-point scale had been achieved. The overall accuracy of the system was estimated at 93.4%.

Suggested Citation

  • Sergey S. Yurochka & Igor M. Dovlatov & Dmitriy Y. Pavkin & Vladimir A. Panchenko & Aleksandr A. Smirnov & Yuri A. Proshkin & Igor Yudaev, 2023. "Technology of Automatic Evaluation of Dairy Herd Fatness," Agriculture, MDPI, vol. 13(7), pages 1-19, July.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1363-:d:1189803
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    References listed on IDEAS

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    1. Walenty Poczta & Joanna Średzińska & Maciej Chenczke, 2020. "Economic Situation of Dairy Farms in Identified Clusters of European Union Countries," Agriculture, MDPI, vol. 10(4), pages 1-22, March.
    2. Habtamu Alem, 2021. "The Role of Technical Efficiency Achieving Sustainable Development: A Dynamic Analysis of Norwegian Dairy Farms," Sustainability, MDPI, vol. 13(4), pages 1-11, February.
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    Cited by:

    1. Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.

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