Remote Sensing, GIS and Artificial Intelligence for Precision Nutrient Management: Advances, Challenges and Future Perspectives

precision nutrient management through predictive modelling, nutrient deficiency detection, digital soil mapping, yield forecasting and intelligent decision-support systems, enabling accurate fertilizer recommendations from large geospatial and sensor-derived datasets (Adewusi et al., 2024; Folorunso et al., 2025) [1,21] . Supporting technologies such as IoT-enabled sensing networks, cloud-based data management and edge computing further facilitate real-time data acquisition and processing, enabling adaptive nutrient management under diverse agroecosystems. Despite these advances, widespread adoption remains constrained by high implementation costs, limited digital infrastructure, inadequate technical expertise, interoperability issues among heterogeneous datasets, sensor calibration requirements and data privacy concerns, particularly in developing countries and smallholder farming systems (Kumar et al., 2024; Getahun et al., 2024) [23,2] . Although Remote Sensing (RS), Geographic Information Systems (GIS) and Artificial Intelligence (AI) have been widely reviewed individually, their integrated application for precision nutrient management remains insufficiently explored. Recent advances in machine learning, UAV-based sensing, digital soil mapping and intelligent decision-support systems have also not been comprehensively synthesized. Current challenges and future research priorities are critically discussed within an integrated framework. This review aims to summarize recent advances in Remote Sensing (RS), Geographic Information Systems (GIS) and Artificial Intelligence (AI) for precision nutrient management; compare sensing technologies, GIS applications and AI-based approaches for nutrient assessment and fertilizer recommendation; discuss current challenges, limitations and emerging opportunities; and identify future research directions for developing climate-smart, sustainable and data-driven nutrient management systems.

Literature Search Methodology

This review was conducted using a structured narrative literature review approach to synthesize recent advances in Remote Sensing (RS), Geographic Information Systems (GIS), Artificial Intelligence (AI) and related digital technologies for precision nutrient management. Relevant literature published between 2015 and 2025, with emphasis on 2020-2025, was retrieved from Web of Science, Scopus, Science Direct, Springer Link, IEEE Xplore, Wiley Online Library, MDPI and Google Scholar. The search employed Boolean operators (AND, OR) with keywords including "Precision Nutrient Management," "Remote Sensing," "GIS," "Artificial Intelligence," "Machine Learning," "Digital Soil Mapping," "Variable Rate Technology," "UAV," "Vegetation Indices," and "Decision Support Systems." Peer-reviewed research articles, review papers, conference proceedings and technical reports relevant to precision nutrient management were included, whereas duplicate, non-English and unrelated publications were excluded. The selected studies were critically evaluated and thematically synthesized to identify technological advances, applications, current challenges and future research directions.

Precision Nutrient Management 3.1 Precision Nutrient Management: Concept and Principles

Precision Nutrient Management (PNM) is a site-specific approach that optimizes fertilizer source, rate, timing and placement according to crop nutrient requirements and field variability. It integrates soil testing, crop monitoring, Remote Sensing (RS), Geographic Information Systems (GIS), GPS, Variable Rate Technology (VRT) and Artificial Intelligence (AI) to improve nutrient-use efficiency and support sustainable agriculture (Koley et al., 2024; Liu et al., 2025) [12,13] . Its major components include soil nutrient assessment, crop nutrient diagnostics, spatial variability mapping, decisionsupport systems, variable-rate fertilizer application and continuous crop monitoring. Site-Specific Nutrient Management (SSNM) synchronizes nutrient supply with crop demand by considering within-field variability. Based on the 4R Nutrient Stewardship framework, it integrates soil fertility, crop growth, weather and yield data to improve nutrient-use efficiency and reduce nutrient losses (Koley et al., 2024) [12] . Recent advances in UAV-based remote sensing, hyperspectral imaging, GIS, artificial intelligence (AI), machine learning and real-time sensor networks have improved nutrient deficiency detection, fertilizer recommendations and adaptive nutrient management, while digital advisory platforms support wider adoption of precision nutrient management (Sangeetha et al., 2024; Howari, 2025; Liu et al., 2025) [55,14,13] .

Nutrient Variability in Agricultural Fields

Spatial variability in soil texture, organic matter, topography, moisture and management practices creates uneven nutrient distribution within fields. Consequently, uniform fertilizer application often causes nutrient deficiencies or excesses, reducing fertilizer-use efficiency and increasing environmental and economic risks (Sangeetha et al., 2024) [8] . Remote sensing, UAVs, hyperspectral imaging, GIS and artificial intelligence improve assessment of spatial nutrient variability by integrating multisource datasets. These technologies enable accurate soil nutrient mapping, management-zone delineation and dynamic fertilizer recommendations for site-specific nutrient management (Howari, 2025; Liu et al., 2025) [14,13] .

Benefits of Precision Nutrient Management

Precision Nutrient Management (PNM) improves nutrient-use efficiency, crop productivity and profitability through sitespecific fertilizer application. Variable-rate technology reduces nutrient losses and environmental pollution, while integrating Artificial Intelligence (AI), Remote Sensing (RS), GIS and decision-support systems enables efficient, sustainable nutrient management (Liu et al., 2025) [13] . Recent evidence demonstrates that AI-enabled precision nutrient management can reduce fertilizer consumption while improving crop yield and overall resource-use efficiency under variable field conditions, making PNM an important component of climate-smart and sustainable agriculture (Sangeetha et al., 2024; Howari, 2025) [8,14] .

Remote Sensing Technologies in Precision Nutrient Management

Remote Sensing (RS) provides rapid, non-destructive and spatially explicit information on crop and soil conditions. By monitoring crop growth, biomass, chlorophyll content and nutrient stress through canopy reflectance, RS enables early nutrient deficiency detection and supports timely, site-specific fertilizer management (Sangeetha et al., 2024; Yang et al., 2025) [8,15] . Recent advances in satellite imagery, unmanned aerial vehicles (UAVs), hyperspectral imaging, proximal sensing and artificial intelligence (AI) have improved nutrient assessment by enabling spatial variability analysis, nutrient deficiency detection, prescription-map generation and variable-rate fertilizer application for efficient nutrient management (Ioja et al., 2023) [16] . Consequently, RS has become an essential component of climate-smart and datadriven agriculture.

Fundamentals of Remote Sensing

Remote sensing acquires information on crops and soils without direct contact by measuring reflected or emitted electromagnetic radiation. Sensors operating in the visible, near-infrared (NIR) and thermal regions assess crop health, biomass, soil moisture and nutrient status. Variations in spectral reflectance enable multispectral, hyperspectral, thermal and LiDAR sensors to identify nutrient-deficient areas and support site-specific fertilizer management (Yang et al., 2025; Sangeetha et al., 2024) [15,8] .

Remote Sensing Platforms

Remote sensing platforms differ in spatial resolution, temporal frequency, operational flexibility and acquisition cost, making them suitable for different scales of precision nutrient management. Platform selection depends on field size, crop type, monitoring objectives and resource availability. Satellite systems provide regular and cost-effective observations for regional and large-scale crop monitoring, whereas unmanned aerial vehicles (UAVs) deliver ultra-high resolution imagery for field-level nutrient assessment. Aircraft-based platforms are mainly employed for specialized surveys and research applications, while ground-based (proximal) sensors provide real-time measurements for crop diagnosis, sensor calibration and variable-rate fertilizer application. Together, these complementary platforms enable accurate monitoring of crop nutrient status, spatial variability and management zones, thereby supporting site-specific fertilizer recommendations and improving nutrient use efficiency (Sangeetha et al., 2024; Gomes et al., 2025; Kuunya et al., 2026) [8,17,18] . [19,20] ;

(Sishodia et al., 2020; Gomes et al., 2025) [58,17] UAV [6,21,22,15] Aircraft-based Sub-meter to several meters

Covers larger areas than UAVs with higher resolution than satellites; accommodates advanced sensors Expensive operation, limited accessibility

Regional agricultural surveys, hyperspectral mapping, research applications (Gomes et al., 2025) [17] Ground [8,23,24]

Types of Sensors

Multispectral sensors are widely used for routine nutrient monitoring, whereas hyperspectral, thermal and LiDAR sensors enhance nutrient deficiency detection, stress assessment and biomass estimation. Their integration with Geographic Information Systems (GIS), Artificial Intelligence (AI) and machine learning improves site-specific nutrient management (Gomes et al., 2025; Yang et al., 2025) [17,15] . Remote sensing employs a variety of sensor technologies to assess crop physiology, nutrient status and environmental conditions for precision nutrient management. Optical (RGB) sensors provide cost-effective information on crop colour, canopy cover and overall plant health but are less sensitive to early nutrient stress (Eskandari et al., 2020) [21] . Multispectral sensors, operating in the blue, green, red, red-edge and nearinfrared (NIR) regions, are widely used for estimating vegetation indices such as NDVI, GNDVI and NDRE to evaluate crop vigor and nitrogen status (Choosumrong et al., 2023; Gomes et al., 2025) [22,17] . Hyperspectral sensors capture hundreds of narrow spectral bands, enabling highly sensitive detection of macro-and micronutrient deficiencies, although they require sophisticated processing and are relatively expensive (Lu et al., 2020; Yang et al., 2025) [6,15] . Thermal sensors assess canopy temperature to detect physiological stress associated with water and potassium deficiencies, while LiDAR provides accurate three-dimensional information on crop architecture, biomass and plant height for precision nutrient management (Eskandari et al., 2020; Yang et al., 2025) [21,15] .

Vegetation Indices for Nutrient Assessment

Vegetation indices (VIs) are mathematical combinations of spectral reflectance values used to assess crop health, chlorophyll content, biomass, canopy vigor and nutrient status. Commonly used indices include the Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), Normalized Difference Red Edge Index (NDRE), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI) and Canopy Chlorophyll Content Index (CCCI), each providing specific advantages for nutrient assessment under different crop and environmental conditions. Integration with UAV imagery and artificial intelligence (AI) further improves nutrient diagnosis and variable-rate fertilizer recommendations (Peticilă et al., 2025; Yang et al., 2025) [48,15] . [15] GRVI (Green-Red Vegetation Index) GRVI =

Green+Red

Chlorophyll monitoring Simple indicator of chlorophyll variation (Haque et al., 2024) [25]

GNDVI (Green Normalized Difference

NIR-Green

NIR+Green

Nitrogen status, chlorophyll estimation More sensitive than NDVI for nitrogen assessment (Yang et al., 2025) [15] SAVI (Soil Adjusted Vegetation Index) SAVI = [17,15] . Nitrogen deficiency is commonly assessed using vegetation indices such as NDVI, GNDVI, NDRE and CCCI, whereas phosphorus, potassium and micronutrient deficiencies are detected through canopy characteristics, thermal responses and hyperspectral signatures. AI-driven multisensor data fusion further enhances nutrient assessment and adaptive precision nutrient management (Peticilă et al., 2025; Yang et al., 2025) [48,15] .

Geographic Information Systems (GIS) in Precision Nutrient Management

Geographic Information Systems (GIS) integrate georeferenced soil, crop, remote sensing and GPS data to analyze spatial variability in soil fertility and crop nutrient status, enabling site-specific fertilizer management and improving nutrient-use efficiency in precision agriculture (Sangeetha et al., 2024) [8] . Recent integration of GIS with remote sensing, unmanned aerial vehicles (UAVs), artificial intelligence (AI), cloud computing and the Internet of Things (IoT) has further strengthened real-time nutrient monitoring and precision decision-making, making GIS a central technology in digital agriculture (Yang et al., 2025) [15] . [11,27,8,28] . [29,15,30] . [8,29,15] . [28,27] . [8,15] .

Fundamentals of GIS

Spatial Variability Analysis

Soil Fertility and Nutrient Distribution Mapping

Geostatistical Techniques

Variable-Rate Fertilizer Application

Integration of GIS with GPS and Remote Sensing

The integration of GIS with GPS and remote sensing has transformed precision agriculture into a comprehensive spatial decision [8,15,31] . [32,15] Variable Rate Technology (VRT)

Development of geo-referenced prescription maps for fertilizer application

Optimizes fertilizer use, improves nutrient use efficiency, reduces production costs and minimizes environmental pollution (Raihan, 2024) [32] Irrigation Management Spatial analysis of soil moisture, crop water requirements and field variability Enhances irrigation efficiency, improves nutrient uptake and conserves water resources (Raihan, 2024; Yang et al., 2025) [32,15] [8,33] . The integration of AI with remote sensing, Geographic Information Systems (GIS), cloud computing and IoT has further enhanced real-time nutrient monitoring and precision decision-making (Yang et al., 2025) [15] .

Artificial Intelligence in Precision

Overview of Artificial Intelligence in Agriculture

Artificial Intelligence refers to computer systems capable of learning, reasoning, pattern recognition, prediction and decision-making. In agriculture, AI integrates machine learning, deep learning, computer vision, remote sensing and sensor technologies to improve crop productivity while reducing production costs and environmental impacts (Liakos et al., 2018) [34] . [8,33] . [33,34] Random Forest (RF) [34,33] Gradient Boosting (GB)

Machine Learning Algorithms

Sequential ensemble learning using weak learners

Fertilizer recommendation, yield prediction Excellent predictive performance and flexibility (Bustami et al., 2025) [33] XGBoost [33,15] Machine learning continues to evolve through advances in ensemble learning and automated machine learning (AutoML).

Its integration with remote sensing, GIS, IoT and cloud-based decision-support systems has significantly improved nutrient prediction, fertilizer management and sustainable crop production (Sangeetha et al., 2024; Yang et al., 2025) [8,15] .

Deep Learning Methods

Deep learning (DL), a subset of artificial intelligence, has significantly advanced precision nutrient management by automatically extracting complex features from multispectral, hyperspectral, UAV, soil sensor and weather datasets without manual feature engineering. Its ability to process highdimensional agricultural data has improved nutrient deficiency detection, crop health assessment, yield prediction and fertilizer recommendation (Kamilaris and Prenafeta-Boldú, 2018; Bustami et al., 2025) [36,33] . [34,33,15] . The integration of deep learning with remote sensing, cloud computing and IoT technologies has enabled real-time nutrient monitoring and intelligent fertilizer management, enhancing the efficiency and sustainability of precision agriculture. [35,33,15] .

AI-Based Fertilizer Recommendation Systems

Artificial intelligence has transformed fertilizer recommendation from conventional advisory approaches to intelligent, site-specific decision-support systems. AI models integrate soil nutrient status, crop growth stage, weather conditions, historical yield records, remote sensing observations and management practices to determine optimal fertilizer rates and support variable-rate fertilizer application (Sangeetha et al., 2024) [8] . Among the available algorithms, Random Forest, XGBoost, CNN and LSTM are widely used because of their high prediction accuracy and ability to analyze complex spatial and temporal datasets. Their integration with remote sensing, IoT and cloud-based platforms enables adaptive fertilizer recommendations that improve nutrient use efficiency while reducing fertilizer costs and environmental impacts (Yang et al., 2025) [15] . [34,33] .

Predictive Modeling of Crop Nutrient Requirements

Predictive modeling is an important application of artificial intelligence in precision nutrient management, enabling proactive estimation of crop nutrient requirements throughout the growing season. AI models integrate soil fertility, historical crop performance, weather conditions, topography, remote sensing observations and management practices to predict nutrient demand and optimize fertilizer application (Bustami et al., 2025; Yang et al., 2025) [33,15] . Machine learning algorithms, including Random Forest, Gradient Boosting, XGBoost and Artificial Neural Networks (ANNs), effectively capture complex interactions among soil, climate and crop variables, providing more accurate nutrient predictions than conventional statistical approaches (Liakos et al., 2018) [34] . The integration of real-time geospatial and sensor data further enables adaptive fertilizer management, improving nutrient use efficiency while reducing fertilizer losses and environmental impacts (Sangeetha et al., 2024; Yang et al., 2025) [8,15] .

Explainable AI for Agricultural Decision Making

Although artificial intelligence models provide highly accurate predictions, many operate as "black-box" systems with limited interpretability. Explainable Artificial Intelligence (XAI) improves model transparency by identifying the factors influencing AI predictions, thereby increasing user confidence and supporting informed agricultural decision-making (Lundberg and Lee, 2017; Arrieta et al., 2020) [37,38] .

Techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) help explain fertilizer recommendations by quantifying the contribution of variables such as soil nutrient status, vegetation indices, weather conditions and crop growth stages. Improved model interpretability enhances the reliability and adoption of AI-based decision-support systems, making XAI an essential component of future precision nutrient management (Bustami et al., 2025) [33] .

Integration of Remote Sensing, GIS and Artificial Intelligence for Precision Nutrient Management

The integration of Remote Sensing (RS), Geographic Information Systems (GIS) and Artificial Intelligence (AI) has transformed precision nutrient management into an intelligent, data-driven approach. Remote sensing monitors crop and nutrient status, GIS analyzes spatial variability and management zones and AI predicts nutrient requirements and fertilizer recommendations. Together, these technologies enable real-time decision-making, improve nutrient-use efficiency, reduce fertilizer losses and support climate-smart, sustainable agriculture (Sangeetha et al., 2024; Yang et al., 2025) [8,15] .

Data Integration and Decision Support Systems

Precision nutrient management integrates satellite and UAV imagery, Geographic Information Systems (GIS), soil, weather, Internet of Things (IoT) and historical crop data through data fusion techniques, improving nutrient assessment accuracy and enabling reliable, site-specific fertilizer recommendations and decision support (Bustami et al., 2025) [33] . Integrated Decision Support Systems (DSS) combine agricultural datasets, machine learning and crop simulation models to generate site-specific nutrient recommendations.

Digital Soil Mapping and Precision Fertilizer Recommendation

Digital Soil Mapping (DSM) combines GIS, remote sensing, geostatistics and AI to generate higher-solution maps of soil properties and nutrient distribution. Geo-referenced soil samples are integrated with environmental variables such as topography, vegetation indices, climate and satellite imagery to predict soil pH, organic carbon, soil moisture, texture and macro-and micronutrient status. Machine learning approaches, including Random Forest, Gradient Boosting, XGBoost and Artificial Neural Networks (ANNs), have substantially improved the accuracy of soil fertility mapping and nutrient prediction (Bustami et al., 2025; Yang et al., 2025) [33,15] . AI integrates soil fertility maps, vegetation indices, weather, crop growth and yield data to generate sitespecific fertilizer recommendations, enabling GPS-guided variable-rate application, improved nutrient-use efficiency and reduced production costs and environmental impacts (Sangeetha et al., 2024; Yang et al., 2025) [8,15] .

Smart Digital Agriculture Platforms

The integration of cloud computing, big data analytics and the Internet of Things (IoT) has further strengthened precision nutrient management by enabling continuous monitoring and real-time decision-making. Cloud platforms provide scalable infrastructure for storing and processing large datasets collected from satellites, UAVs, weather stations, farm machinery and IoT sensors. Big data analytics identifies complex relationships among soil, crop and environmental variables, improving nutrient prediction and fertilizer optimization (Sangeetha et al., 2024; Bustami et al., 2025) [8,33] . IoT-enabled sensors continuously monitor soil moisture, nutrient availability, temperature and weather conditions, transmitting real-time data to cloud-based Artificial Intelligence (AI) [8,33] systems. These platforms enable adaptive fertilizer recommendations, early nutrient stress detection, improved fertilizer-use efficiency and sustainable crop production (Yang et al., 2025) [15] . [39,15] . Future precision nutrient management will increasingly rely on the seamless integration of RS, GIS [8,33,15] . [39,15]

Emerging

Applications in Major Crops

Precision nutrient management (PNM) improves nutrient use efficiency, crop productivity and environmental sustainability through site-specific fertilizer management. By integrating remote sensing, Geographic Information Systems (GIS), artificial intelligence (AI) and variable-rate technologies, PNM optimizes nutrient application across cereals, oilseeds, pulses, horticultural and plantation crops according to crop requirements and field variability (Sangeetha et al., 2024; Yang et al., 2025) [8,15] .

Cereals

Precision nutrient management in cereal crops, including rice, wheat and maize, enhances nutrientuse efficiency through remote sensing, Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs) and artificial intelligence (AI), enabling accurate nutrient deficiency detection, variablerate fertilizer application, improved grain yield and quality (Liakos et al., 2018; Bustami et al., 2025; Yang et al., 2025; Gomes et al., 2025) [34,33,15,17] .

Oilseed and Pulse Crops

In oilseed and pulse crops, precision nutrient management improves nutrient-use efficiency, seed quality and productivity through site-specific fertilizer recommendations. Geospatial technologies and artificial intelligence (AI) optimize nitrogen, phosphorus, sulfur, potassium and micronutrient management across diverse agroecological conditions (Liakos et al., 2018; Mulla, 2013; Bustami et al., 2025; Sangeetha et al., 2024) [34,29,33,8] .

Challenges

Despite rapid advances in Remote Sensing (RS), Geographic Information Systems (GIS), Artificial Intelligence (AI) and associated digital technologies, several challenges continue to limit the widespread implementation of precision nutrient management (PNM). Addressing these constraints is essential to improve nutrient-use efficiency, technology accessibility and sustainable agricultural production (Finger et al., 2019) [44] .

Data Quality and Integration Challenges

Effective PNM depends on accurate, standardized and interoperable datasets. However, sensor calibration errors, cloud contamination in satellite imagery, missing observations, inconsistent sampling and interoperability issues among RS, GIS, AI and Internet of Things (IoT) platforms reduce prediction accuracy and limit decision-support performance. Advanced geospatial processing and AI models also require substantial computational resources (Shi et al., 2016; Wolfert et al., 2017) [45,40] . [44,46,47,48] .

AI Model and Validation Challenges

Most AI and machine learning models are developed using region-specific datasets, limiting their applicability across different crops, soils and climatic conditions. Climate variability further influences nutrient dynamics and crop demand, emphasizing the need for standardized datasets, explainable AI, adaptive prediction models and multi-location validation (Van Klompenburg et al., 2020; Calvin et al., 2023) [49, 50] .

Policy and Data Governance Challenges

Data ownership, interoperability, cybersecurity, privacy and technology certification remain major concerns affecting user confidence. Standardized digital agriculture frameworks and secure data governance are needed to facilitate data sharing and technology integration (Wolfert et al., 2017; Khanam et al., 2020) [40,51] .

Capacity Building and Technology Adoption

Limited digital literacy, insufficient extension support and weak university-industry collaboration continues to hinder PNM adoption. Strengthening farmer training, public-private partnerships and capacity-building initiatives will be essential for expanding sustainable digital agriculture (Finger et al., 2019; Rose et al., 2021; Khanam et al., 2020) [44,47,51] .

Future Perspectives

Future advances in precision nutrient management will be driven by the convergence of Artificial Intelligence (AI), Remote Sensing (RS), Geographic Information Systems (GIS), the Internet of Things (IoT), cloud computing, robotics and advanced sensing technologies. These innovations are expected to enable intelligent, predictive and site-specific nutrient management, improving agricultural productivity and environmental sustainability (FAO, 2022; Ali et al., 2025) [52,9] .

Intelligent Digital Nutrient Management

Future AI-driven decision-support systems will integrate satellite imagery, UAV observations, IoT sensors, weather information, soil databases and crop growth data to provide real-time fertilizer recommendations. Explainable AI, digital twins, cloud computing, edge computing and autonomous machinery will improve prediction accuracy, transparency and operational efficiency (Duckett et al., 2018; Reichstein et al., 2019; Verdouw et al., 2021; Kairouz et al., 2021) [53,54,55,56] .

Climate-Smart Agriculture

The integration of AI, GIS, RS and climate forecasting will support adaptive nutrient management while reducing nutrient losses and greenhouse gas emissions. Standardized data frameworks, affordable sensing technologies and supportive policies will accelerate climate-smart and sustainable agriculture (Jakku et [57,28,58,48,52] .

Emerging Digital Technologies

Emerging technologies, including digital twins, advanced hyperspectral sensing, robotics, autonomous fertilizer applicators and edge computing, will enhance continuous monitoring, predictive analytics and automated nutrient management, improve resource-use efficiency and reduce production costs (Duckett et al., 2018; Verdouw et al., 2021; Reichstein et al., 2019; Lord, 2024) [53,55,54,59] . [60,61,49,62,63] .

Research and Standardization

Inclusive and Sustainable Adoption

Affordable technologies designed for smallholder farmers, stronger extension services and public-private partnerships will be essential for the widespread adoption of precision nutrient management. These efforts will enhance nutrient-use efficiency, strengthen climate resilience and contribute to sustainable agricultural development and global food security (FAO, 2022; Cassman and Grassini, 2020; Sabir and Ozkececi, 2025) [52,61,63] .

Conclusion

Precision nutrient management (PNM) has emerged as a key approach for improving crop productivity, nutrient-use efficiency and environmental sustainability through the integration of Remote Sensing (RS), Geographic Information Systems (GIS), Artificial Intelligence (AI) and other digital technologies. This review highlights the roles of remote sensing, digital soil mapping, machine learning, variable-rate technology and intelligent decision-support systems in enabling accurate nutrient assessment and site-specific fertilizer management. Emerging technologies, including the Internet of Things (IoT), cloud computing, digital twins and explainable AI, further strengthen real-time monitoring and predictive nutrient management. Despite challenges related to data integration, affordability and technology adoption, continued innovation, standardized data frameworks and interdisciplinary collaboration will accelerate the development of climate-smart, sustainable and data-driven precision nutrient management systems.

Limitations

This review has several limitations. First, it is based on a structured narrative review rather than a quantitative metaanalysis and, therefore, does not estimate overall effect sizes or publication bias. Second, most reviewed studies are regionand crop-specific, limiting the generalizability of findings across diverse agroecological conditions. Third, the performance of Remote Sensing (RS), Geographic Information Systems (GIS) and Artificial Intelligence (AI) depends on data quality, sensor calibration and model validation. Finally, many advanced technologies have been evaluated mainly under research or pilot-scale conditions. Future research should emphasize multi-location validation, standardized protocols and long-term field evaluations to improve the reliability and practical applicability of precision nutrient management.

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