AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems
Year: 2026
Type: article
Abstract: Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.
Authors: Olufisayo Obebe, Ebenezer Akinola
Institution: Kentucky State University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDGs: Zero hunger, Climate action
Open Access status: hybrid
OpenAlex ID: W7214553198