Optimizing Water and Fertilizer Use in Agriculture Through AI-Driven IoT Networks: A Comprehensive Analysis

Abstract

This research investigates the implementation and effectiveness of artificial intelligence (AI) and Internet of Things (IoT) networks in optimizing water and fertilizer usage in agricultural systems. Through analysis of multiple case studies and field experiments, we demonstrate that AI-driven IoT networks can reduce water consumption by 20-35% and fertilizer use by 15-30% while maintaining or improving crop yields. The study examines various sensor technologies, machine learning algorithms, and control systems, providing a framework for large-scale implementation of smart farming solutions. Our findings indicate that the integration of AI-IoT systems in agriculture not only promotes resource efficiency but also contributes to sustainable farming practices and improved economic outcomes for farmers.