AI-Driven Smart Fertigation: Integrating IoT and Machine Learning for Sustainable Precision Agriculture

Abstract

Smart fertigation, integration of irrigation and fertilization through precision agriculture is revolutionizing modern farming with Internet of Things (IoT) and Machine Learning (ML) technologies. These innovations enhance water and nutrient management, minimize human intervention, and promote sustainability. This study reviews recent advancements in IoT-based fertigation systems, emphasizing ML models such as Support Vector Machines, Extreme Gradient Boosting and deep learning techniques for predictive analysis. Additionally, real-time sensor networks, cloud computing, and optimization methods like fuzzy logic are explored for their role in improving resource efficiency. A structured literature review identifies key challenges, including scalability, interoperability, and cost constraints. Comparative evaluations highlight the effectiveness of various ML models in precision fertigation. The findings demonstrate the potential of AI-driven fertigation for enhancing agricultural productivity and sustainability. Future research should focus on hybrid AI models, blockchain-based secure data management, and large-scale IoT deployment to further optimize smart fertigation systems.