Utilization of intelligent information technologies for resources management in agricultural enterprises
DOI:
https://doi.org/10.31734/agroengineering2024.28.173Keywords:
digital technologies, information technologies, agrarian enterprises, agriculture, crop production, animal husbandry, artificial intelligence, innovative development, digitizationAbstract
The demand for innovative agricultural technologies and an understanding of the conditions of market globalization are increasingly critical. This article analyzes the role of artificial intelligence (AI) in optimizing resource management within agricultural enterprises through big data analysis. It demonstrates the essential need for agribusinesses to develop and enhance rapid and efficient scenario modeling for decision-making, integrating these models to assess future benefits, expected profits, and forecast payback periods. The importance of implementing information technologies aimed at innovative processes in the agricultural sector, both at a general level and within specific subsectors and components, is emphasized. This implementation should occur through state programs, particularly concerning the technical and technological aspects of agricultural production processes. Such measures promote the development of high-tech activities and facilitate the transition from a low-resource economy to a high-tech, innovative economy. The research results provide a comprehensive overview of existing AI technologies that have proven effective in solving problems within agriculture and agribusiness. These technologies are expected to advance significantly over the next five to seven years, allowing businesses to gain competitive advantages and achieve substantial economic benefits. The use of AI and robots in agriculture addresses several key challenges, including alleviating labor shortages, reducing the harmful impact of chemicals on humans and the environment, increasing crop yields, improving enterprise productivity, and lowering the costs of agricultural production. Future research may focus on a more detailed examination of various AI technologies and methods to identify their optimal applications across different areas of human activity, including agriculture and agribusiness. This will include exploring effective combinations of these technologies and establishing an optimal internal structure, encompassing both mathematical frameworks and software coding.
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