IMPLEMENTATION OF A SOLUTION BASED ON MICROSOFT AZURE INFRASTRUCTURE FOR AGRIBUSINESS MANAGEMENT

DOI 10.31673/2412-4338.2025.027357

Abstract

The introduction of modern cloud technologies based on artificial intelligence (AI) in the agro-industrial complex (AIC) is not only an innovative trend but also a prerequisite for its development and overcoming the challenges of today, given the constant growth in demand for food and the need to preserve natural resources. AI technologies are becoming a critical tool for ensuring sustainable development and efficient management of the agricultural sector. They also contribute to the growth of productivity and accuracy of management decision-making and enable rapid response to the rapid changes in the farm sector due to the digital transformation of this industry and the economy as a whole. The article presents typical machine learning (ML) tasks that can be applied in the agricultural sector to improve business processes. Based on the Microsoft Azure cloud platform, an architecture has been built that provides modularity, scalability, flexibility, security, and integration of various components to optimize business processes in the agro-industrial complex and effective management in general. Azure Machine Learning is used to create and deploy forecasting and optimization models, which will help representatives of the agricultural sector to adapt to changes in climate conditions and weather events quickly, predict yields, field conditions, market prices, etc., and provide flexible management of business processes through interactive dashboards and APIs (RESTful API settings). In addition, recommendations for further improvement of the forecasting solution in the agricultural sector using cloud technologies by small farms with limited resources are provided. The implementation of the proposed solution based on Microsoft Azure infrastructure will be a valuable resource for professionals involved in the digital transformation of the country's agricultural sector, developers of cloud solutions to support various business processes in the agro-industrial complex, in particular for the implementation of typical machine learning (ML) tasks.

Keywords: cloud infrastructure, artificial intelligence, machine learning, agricultural industry, digital transformation, big data.