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Responsible Production and Consumption in Agriculture 4.0 Based on Deep Learning for Sustainable Development

In: Food Security in the Economy of the Future

Author

Listed:
  • Yerlan B. Zhailauov

    (“Rational Solution” LLP)

  • Natalia V. Przhedetskaya

    (Rostov State University of Economics)

  • Vasiliy I. Bespyatykh

    (Vyatka State University)

Abstract

The paper aims to explore the international experience and justify the benefits of responsible production and consumption in agriculture 4.0 based on deep learning for sustainable development. To determine the contribution of responsible production and consumption to food security, this paper applies the method of regression analysis. This method is used to find the dependence of the results in the implementation of SDG 2 on the achievements in the implementation of SDG 7. To determine how responsible production and consumption in agriculture can be integrated into a system of 17 SDGs, the authors conducted a qualitative study that considers deep learning opportunities. As a result, it is demonstrated that responsible production and consumption in agriculture 4.0 based on deep learning can improve the sustainability of all production and distribution processes: from the involvement of factors of production (shown using labor, land, and capital as an example) to their transformation into finished products and their sale, as well as the disposal of waste. The theoretical significance of these results is that they substantiate the benefits of responsible production and consumption in agriculture 4.0 based on deep learning for sustainable development and reveal the potential of systemic implementation of all 17 SDGs in the agricultural economy.

Suggested Citation

  • Yerlan B. Zhailauov & Natalia V. Przhedetskaya & Vasiliy I. Bespyatykh, 2023. "Responsible Production and Consumption in Agriculture 4.0 Based on Deep Learning for Sustainable Development," Springer Books, in: Elena G. Popkova & Bruno S. Sergi (ed.), Food Security in the Economy of the Future, chapter 0, pages 139-146, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-23511-5_15
    DOI: 10.1007/978-3-031-23511-5_15
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