Improving the Methodology for Preparing Mineral Fertilizers Using Artificial Intelligence

Abstract

The increasing demand for sustainable agricultural productivity has intensified the need for advanced methods in mineral fertilizer preparation that ensure efficiency, environmental safety, and economic viability. Traditional fertilizer production relies heavily on fixed formulations, empirical adjustments, and manual quality control, which often fail to respond adequately to dynamic soil conditions, crop-specific nutrient requirements, and environmental constraints. This study explores the integration of artificial intelligence (AI) into the methodology of preparing mineral fertilizers, with the aim of optimizing formulation processes, improving nutrient-use efficiency, and reducing ecological risks. By conceptualizing AI as a methodological tool rather than a mere technological add-on, the paper proposes an AI-assisted framework that enhances decision-making across formulation design, process control, and performance evaluation. Using a mixed analytical approach that combines simulation-based modeling, data-driven optimization, and comparative analysis, the study demonstrates how AI techniques can significantly improve accuracy, adaptability, and scalability in fertilizer preparation. The findings indicate that AI-supported methodologies outperform conventional approaches in terms of formulation precision, reduction of nutrient loss, and responsiveness to variable agricultural inputs. The study contributes to the emerging field of intelligent agro-industrial systems by offering a structured methodological perspective on AI-driven fertilizer preparation and outlining implications for sustainable agricultural development.