Automation of Agriculture Based on Deep Learning: Modeling and Management to Improve Quality and Efficiency

Natalia V. Przhedetskaya (), Eleonora V. Nagovitsyna (), Victoria Yu. Przhedetskaya and Ksenia V. Borzenko
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Natalia V. Przhedetskaya: Rostov State University of Economics
Eleonora V. Nagovitsyna: Vyatka State University
Victoria Yu. Przhedetskaya: Ministry of Health of the Russian Federation
Ksenia V. Borzenko: Rostov State University of Economics

Chapter Chapter 14 in Food Security in the Economy of the Future, 2023, pp 131-137 from Springer

Abstract: Abstract The research purpose is related to the modeling of automation of agriculture and identifying the prospects for improving the management of this process based on deep learning to improve the quality and efficiency of the agricultural economy of Russia. To compare the contribution of agricultural automation and sown area to food quality and efficiency, this research conducts a regression analysis of the dependence of quality and efficiency (production index, share of profitable organizations, and profitability) on automation (investment in fixed capital) and sown area in Russia in 2012–2020. As a result, it has been substantiated that technology is a more important factor in production than land for food quality and efficiency. Automation makes the greatest contribution to food security and is therefore preferred. Further automation of agriculture is advisable based on deep learning because it will provide more pronounced results in the form of increased food quality and increased efficiency of agricultural entrepreneurship. The practical significance of this research is related to the fact that the proposed recommendations allow for improving the quality and efficiency of agriculture and successfully implementing SDG 2 through the automation of agriculture based on deep learning.

Keywords: Automation; Agriculture; Deep learning; Modeling; Management; Food quality; Efficiency of agricultural entrepreneurship; C32; C35; D61; K13; L15; O13; O14; Q18 (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_14

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DOI: 10.1007/978-3-031-23511-5_14

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