In the rapidly evolving landscape of the AI economy, developing countries are increasingly turning to intellectual management information systems to bolster the quality of their agricultural products. A recent study published in the *Proceedings on Engineering Sciences* (Proceedings of Engineering Sciences) sheds light on the key features and prospects of these systems, offering a roadmap for enhancing product quality in African nations.
Led by Gulmira K. Omurkulova of the Kyrgyz State Technical University named after I. Razzakov in Bishkek, Kyrgyzstan, the research identifies several critical directions for implementing these systems. These include monitoring and forecasting climate conditions, supporting knowledge and skills through automated solutions, and facilitating online commerce by determining quality parameters. “The main directions for the implementation of the considered systems include monitoring and forecasting climate and other indicators of product quality; support of knowledge and skills in this sphere through consultation and automatized solution of production tasks; informing about the level of product quality of the given sector; and online commerce, which systems of management allow determining the parameters of quality,” Omurkulova explains.
The study highlights countries like Kenya, South Africa, and Nigeria as leaders in adopting these technologies, attributing their success to factors such as robust technological infrastructure and favorable government policies. However, the research also underscores significant challenges, particularly in financing and technological support, which hinder widespread adoption.
The implications for the energy sector are profound. As agricultural production becomes more efficient and product quality improves, the demand for energy resources will likely shift. Farmers and agribusinesses equipped with advanced management information systems can optimize their operations, reducing energy consumption and enhancing sustainability. This, in turn, could drive innovation in renewable energy solutions tailored to the agricultural sector.
Omurkulova’s research suggests that the future of agricultural production in developing countries lies in the strategic implementation of these systems. By leveraging AI and data analytics, farmers can make informed decisions that enhance product quality and market competitiveness. “The key problem in this sphere is financing and technological support for increased product quality of agricultural production in African countries,” she notes, emphasizing the need for investment and collaboration.
As the AI economy continues to grow, the insights from this study could shape future developments in agricultural technology and energy efficiency. By addressing the identified challenges and capitalizing on the opportunities presented by management information systems, developing countries can pave the way for a more sustainable and prosperous future. The research, published in the *Proceedings on Engineering Sciences*, serves as a crucial stepping stone in this journey, offering valuable guidance for policymakers, technologists, and agricultural stakeholders alike.