Precision agriculture technologies for soil site-specific nutrient management: A comprehensive review
Niharika Vullaganti, Billy G. Ram, Xin Sun
Abstract
Amidst the growing food demands of an increasing population, agricultural intensification frequently depends on excessive chemical and fertilizer applications. While this approach initially boosts crop yields, it effects long-term sustainability through soil degradation and compromised food quality. Thus, prioritizing soil health while enhancing crop production is essential for sustainable food production. Site-Specific Nutrient Management (SSNM) emerges as a critical strategy to increase crop production, maintain soil health, and reduce environmental pollution. Despite its potential, the application of SSNM technologies remains limited in farmers' fields due to existing research gaps. This review critically analyzes and presents research conducted in SSNM in the past 11 years (2013–2024), identifying gaps and future research directions. A comprehensive study of 97 relevant research publications reveals several key findings: a) Electrochemical sensing and spectroscopy are the two widely explored areas in SSNM research, b) Despite numerous technologies in SSNM, each has its own limitation, preventing any single technology from being ideal, c) The selection of models and preprocessing techniques significantly impacts nutrient prediction accuracy, d) No single sensor or sensor combination can predict all soil properties, as suitability is highly attribute-specific. This review provides researchers, technical personnel in precision agriculture, and farmers with detailed insights into SSNM research, its implementation, limitations, challenges, and future research directions. • Site-specific nutrient management promotes sustainable and site-specific agricultural practices for better crop yields and environmental health. It is also necessary to address soil variability and improve the accuracy of nutrient prediction technologies. • This review analyzes research on site-specific nutrient management from the past 11 years (2013–2024). • Provides detailed insights to the farmers and research community about the current site-specific nutrient management technologies based on machine learning and deep learning, their utilization and limitations. • Offers future research directions for researchers and technical personnel in precision agriculture.