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Roadmap for the Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning in the Economy of the Future by 2030

In: Food Security in the Economy of the Future

Author

Listed:
  • Nazgul S. Daribekova

    (Abylkas Saginov Karaganda Technical University)

  • Marina A. Sanovich

    (Vyatka State University)

  • Nadezhda K. Savelyeva

    (Vyatka State University)

  • Tatiana A. Dugina

    (Volgograd State Agricultural University)

  • Anastasia I. Smetanina

    (Institute of Scientific Communications (ISC-Group LLC))

Abstract

The paper aims to develop a roadmap for the transition from digital agriculture to agriculture 4.0 based on deep learning in the economy of the future by 2030. In drawing up the roadmap, the work is based on the planning method and the program-targeted approach. The authors present their vision of the transition from digital agriculture to agriculture 4.0 based on deep learning until 2030 and the details of its practical implementation. The developed roadmap for the transition from digital agriculture to agriculture 4.0 based on deep learning in the economy of the future until 2030 formed the scientific basis for the organization and management of this process. The roadmap will be useful for countries with high digital competitiveness, particularly those included in the IMD’s 2021 ranking (64 developed and developing countries). Due to the high level of detail, the proposed roadmap is ready for practical use. Its advantage is a demonstration of the distribution of powers and responsibilities of state regulators of the agricultural economy and agricultural entrepreneurship.

Suggested Citation

  • Nazgul S. Daribekova & Marina A. Sanovich & Nadezhda K. Savelyeva & Tatiana A. Dugina & Anastasia I. Smetanina, 2023. "Roadmap for the Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning in the Economy of the Future by 2030," Springer Books, in: Elena G. Popkova & Bruno S. Sergi (ed.), Food Security in the Economy of the Future, chapter 0, pages 123-130, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-23511-5_13
    DOI: 10.1007/978-3-031-23511-5_13
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