Digitalization of the agrarian sector: the opportunities of artificial intelligence

Raushan Yesbergen, A. Y. Kultanova

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.46666/2026-2.2708-9991.03
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

The introduction of artificial intelligence (AI) technologies in agriculture is regarded as an important factor in increasing the efficiency of the agro-industrial complex, ensuring the rational use of resources, and reducing risks. The application of AI-based solutions within the digital transformation of the agrarian sector of the Republic of Kazakhstan is becoming crucial for improving productivity and enhancing the competitiveness of agricultural production. The objective - is to substantiate the prospects for the adoption of algorithmic intelligence mechanisms in the digitalization of the sector under the conditions of the republic’s economic diversification. Methods – analysis, comparison, and benchmarking were employed to identify priority areas of digital modernization and assess their impact on the development of the AIC, evaluate the effectiveness of digital intelligence tools, and support optimal decision-making. Statistical data processing and monitoring were used to systematize and interpret information flows. Results – the study identifies the main areas of application of artificial intelligence in agriculture and analyzes its impact on the efficiency of production processes. A significant part of the research involved conducting a sociological survey of agricultural producers in the Aktobe Region, which made it possible to collect empirical data on the challenges and potential opportunities for improving the agrarian sector during the period of digital transformation. Conclusions – digital innovative systems represent algorithms for the remote assessment of soil conditions, water resource management, and the monitoring of livestock and agricultural crops. Agricultural enterprises can obtain information on crop structure and livestock health, increase profitability, improve working conditions, and reduce environmental impacts. The complexity of integrating machine learning methods requires a careful approach and strategic planning. In the future, artificial intelligence will continue to evolve and play a key role in business transformation, contributing to the creation of intelligent and sustainable entrepreneurial ecosystems.