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

Anastasia A. Sozinova (), Aigul S. Daribekova, Irina P. Lapteva () and Maria V. Makarova ()
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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

Chapter Chapter 9 in Food Security in the Economy of the Future, 2023, pp 85-92 from Springer

Abstract: 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.

Keywords: Food security; Agricultural economy; Agriculture 4.0; Cultivation of plants; High-tech agricultural innovations; Food-importing countries; Deep learning; F52; Q13; Q15; Q16; Q17; Q55 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-23511-5_9

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DOI: 10.1007/978-3-031-23511-5_9

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