Methodology for assessing the development of artificial intelligence in agriculture

Issue № 6, 2025, article № 6, pages 65-73

Section: Economic mechanism of managing

Language: Russian

Original language title: МЕТОДОЛОГИЯ ОЦЕНКИ РАЗВИТИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В СЕЛЬСКОМ ХОЗЯЙСТВЕ

Keywords: ARTIFICIAL INTELLIGENCE, AI ASSESSMENT METHODOLOGY, AGRICULTURE, SCORING AND RATING METHODOLOGY

Abstract: The article examines methods and proposes a scoring and rating methodology for evaluating the development of AI in agricultural systems across nine countries (USA, China, Japan, Australia, Germany, Brazil, India, Algeria, and Russia). This methodology includes an analysis of 10 key areas of AI application, such as precision farming, robotics, data analysis, water resource management, pest control, genetics and breeding, livestock farming, logistics and marketing, personnel training, and quality control of products. The obtained results show that the USA and China demonstrate the highest scores across all areas, particularly in precision farming, robotics, and data analysis. Developing countries like India and Brazil are gradually adopting AI but face limitations due to insufficient infrastructure and investments. Algeria is at the initial stage of AI implementation in agriculture. Russia holds moderate positions, with scores ranging from 5 to 6 in most areas, where the main successes are associated with precision farming, data analysis, and genetics, but lagging behind in robotics and automation. The prospects for Russia are described as significant, given the vast agricultural land areas, state digitalization programs, and the development of scientific research, which create potential for growth. Among the shortcomings are low investments, the absence of a unified digital platform, internet issues in rural areas, and the unpreparedness of many agricultural producers to adopt costly technologies. Overall, Russia demonstrates significant progress in the field of AI implementation in agriculture, but to reach the level of global leaders, additional investments, infrastructure improvements, and government support are needed. By 2030, an increase in the share of digital technologies in the agricultural sector is planned, which could enhance productivity, reduce costs, and make agriculture more sustainable.

Authors: SHabanov Timofei IUrevich, Kopchenov Aleksei Aleksandrovich