Mirzoieva Tetiana, Doctor of Economics, Professor, Professor of the Department of Economy, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

ORCID ID: 0000-0002-0034-6138
e-mail: mirzoeva_tetiana@nubip.edu.ua

Harbut Maryna, Senior specialist of the department of procurement of packaging materials, PrJSC MHP

e-mail: marinaharbut@gmail.com

Gutsul Tetiana, PhD in Economics, associate Professor, Associate Professor of the Department of Economy, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

ORCID ID: 0000-0002-1826-240X
e-mail: gutsul_tetiana@nubip.edu.ua

Artificial Intelligence as an Innovative Tool for Risk Management in Agribusiness

Abstract. Introduction. The article briefly describes the current state of development of artificial intelligence. The research was conducted using the example of PrJSC “Zernoprodukt MHP”, based on the fact that the agricultural holding “Myronivskyi Hliboprodukt” actively implements artificial intelligence in various areas of its business. 

Purpose.  Justification of the possibility of minimizing the risks of production activities, especially in the process of growing agricultural crops, through the use of artificial intelligence. 

Results. The algorithm of a risk monitoring system for crop production using artificial intelligence is presented. The authors focus on the potential for minimizing production risks through an integrated management system designed to cover such areas as risk forecasting, resource optimization, pest and disease monitoring, logistics management, and strategic planning. The key stages of this system are described in detail. The focus is on the use of computerized systems to identify relationships between factors such as weather conditions, soil quality, and productivity. Based on the data collected, it is possible to accurately calculate the amount of fertilizer needed for each area of the field. In addition, the information collected can be used to apply various artificial intelligence models to predict business risks. Implementing a pest and disease monitoring system using artificial intelligence can significantly improve production efficiency and quality, as well as minimize the risk of crop loss and crop protection costs. It is noted that once the model is successfully implemented and its accuracy is verified, it can be used for real-time risk prediction. 

Conclusions. The integration of artificial intelligence into agricultural production will enable producers to monitor crop and soil conditions, thereby facilitating the development of measures to mitigate the risk of crop failure. In addition, AI-based systems will allow for the consideration of various environmental and agronomic factors, improving the ability to predict risks associated with crop production. Furthermore, the optimization of resource use through the implementation of AI will help increase the efficiency of agricultural production processes while reducing the environmental impact of agricultural activities.

Keywords: innovation; artificial intelligence; risks; agribusiness; risk management; risk minimization.

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