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A New Level of Food Security as a Result of the Transition of Food-Importing Countries to Agriculture 4.0 Based on Deep Learning

In: Food Security in the Economy of the Future

Author

Listed:
  • Anastasia A. Sozinova

    (Vyatka State University)

  • Aigul S. Daribekova

    (Abylkas Saginov Karaganda Technical University)

  • Irina P. Lapteva

    (Vyatka State University)

  • Maria V. Makarova

    (Vyatka State University)

Abstract

The paper explores the contribution to food security and food import substitution achieved by food-importing countries from the transition to agriculture 4.0 based on deep learning. The research is based on the example of countries with the highest share of food imports in the structure of merchandise imports in 2020–2021. The authors determine the dependence of growth in the global food security index and food imports on the growth of big data and analytics. The five-year trend (increase) is estimated to account for the progress made in agriculture 4.0; data for 2017 and 2021 are considered. As a result, the authors substantiate that the combined benefits of deep learning in agriculture 4.0 allow systematically achieving deficiency-free basic agricultural products and food import substitution (i.e., move to a new level of food security). The contribution of this work to the literature lies in proving the preference for high-tech innovations, such as deep learning (over low-tech innovations such as precision farming and open ground), in agriculture.

Suggested Citation

  • Anastasia A. Sozinova & Aigul S. Daribekova & Irina P. Lapteva & Maria V. Makarova, 2023. "A New Level of Food Security as a Result of the Transition of Food-Importing Countries to Agriculture 4.0 Based on Deep Learning," Springer Books, in: Elena G. Popkova & Bruno S. Sergi (ed.), Food Security in the Economy of the Future, chapter 0, pages 85-92, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-23511-5_9
    DOI: 10.1007/978-3-031-23511-5_9
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