Work

Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review

Year: 2025

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2025.3594361

Authors: Jian-Jun Liu, Hao Wu, Imran Riaz

Institutions: Baoshan University, Mirpur University of Science and Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,075