{"title":"Precision agriculture technologies for soil site-specific nutrient management: A comprehensive review","authors":"Niharika Vullaganti, Billy G. Ram, Xin Sun","doi":"10.1016/j.aiia.2025.02.001","DOIUrl":null,"url":null,"abstract":"<div><div>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 remain 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, and technical personnel in precision agriculture, and farmers with detailed insights into SSNM research, its implementation, limitations, challenges, and future research directions.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 147-161"},"PeriodicalIF":8.2000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence in Agriculture","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589721725000200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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 remain 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, and technical personnel in precision agriculture, and farmers with detailed insights into SSNM research, its implementation, limitations, challenges, and future research directions.