European Journal of Sustainable Agroecosystems

Background: Precision agriculture demands rapid, non-destructive, and spatially explicit diagnostics of crop nutrient status to optimize fertilizer application, minimize environmental degradation, and maximize yield. Unmanned Aerial Vehicles (UAVs) equipped with multispectral and hyperspectral sensors have emerged as a pivotal bridge between point-based in-situ leaf sampling and coarse-resolution satellite remote sensing. By leveraging canopy reflectance properties across discrete electromagnetic wavebands, UAV-derived Vegetation Indices (VIs) offer an indirect proxy for biochemical and structural traits, particularly chlorophyll content and nitrogen status.

Objective: This review critically evaluates the peer-reviewed literature published between 2015 and 2025 regarding the application, accuracy, limitations, and future trajectory of UAV-derived VIs for precision nutrient diagnostics across diverse cropping systems.

Methodology: A systematic literature search was executed across major scholarly databases (Scopus, Web of Science, ScienceDirect, and IEEE Xplore). Applying a rigorous PRISMA-compliant selection pipeline, 142 high-quality peer-reviewed articles were isolated and critically reviewed for thematic synthesis, methodological robustness, and comparative performance.

Major Findings: The synthesis reveals that while traditional indices like the Normalized Difference Vegetation Index (NDVI) remain ubiquitous, they suffer from catastrophic saturation at high leaf area index (LAI) values and display high sensitivity to soil background interference during early vegetative growth stages. Conversely, red-edge shifted indices—such as the Normalized Difference Red Edge (NDRE) index and the Modified Chlorophyll Absorption in Reflectance Index (MCARI)—demonstrate significantly higher fidelity for mid-to-late season nitrogen diagnostics due to the deep penetration of red-edge wavelengths into the crop canopy. Furthermore, the integration of non-linear machine learning algorithms (e.g., Random Forest, Support Vector Machines, and Convolutional Neural Networks) markedly outperforms traditional parametric regressions by decoupling structural canopy attributes from biochemical signals.

Critical Insights & Gaps: Significant knowledge gaps persist regarding the cross-site transferability of empirical models, the atmospheric and radiometric calibration standardization across heterogeneous lighting conditions, and the decoupling of co-occurring abiotic stresses (e.g., confounding nitrogen deficiency with drought or disease).

Conclusion: UAV-derived VIs represents an indispensable tool for actionable nutrient management, yet transitioning from academic diagnostic capability to scalable, automated, real-time decision-support systems requires standardized radiometric workflows and robust, hybrid physical-biochemical modeling approaches.