
Advanced technologies for smart fertilizer management in agriculture: A Review
Author(s) -
Jian-Jun Liu,
Hao Wu,
Imaran Riaz
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3594361
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Precision fertilization has emerged as a critical component of modern smart agriculture, providing a transformative approach to optimizing nutrient application, enhancing crop productivity, and minimizing environmental harm while improving resource use efficiency. However, key challenges hinder its widespread adoption, including regional disparities in data quality, scalability constraints in low-resource settings, regulatory inconsistencies, and the high costs of technology adoption for smallholder farmers. This article reviews current challenges and examines proposed solutions, with a focus on precision fertilization enabled by advanced technologies such as geographic information systems, global positioning systems, the internet of things, artificial intelligence and machine learning algorithms, and unmanned aerial and ground vehicles for fertilizer management. These innovations have facilitated the development of site-specific nutrient management systems, real-time soil and crop monitoring, and automated variable-rate application strategies, all of which demonstrate superior efficiency compared to conventional blanket fertilization methods. Additionally, the study explores key applications of precision fertilization, including data-driven fertilizer recommendations, aerial crop monitoring, real-time nutrient deficiency prediction, automated and optimized fertilization systems, and organic and eco-friendly nutrient management practices. Future research directions highlight the need for integrating multi-sensor feedback loops, expanding open-access agronomic datasets, and advancing eco-friendly, AI-driven, and fully automated fertilization solutions. In conclusion, modern precision fertilization technologies hold significant potential to address existing challenges, offering a pathway toward resilient and sustainable agricultural systems that align with global food security and environmental conservation objectives.
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