Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: A Systematic Review and Framework for Field-Scale Validation

Magnetic field (MF) technologies have been applied in agriculture for decades. However, they have not achieved mainstream adoption, partly because no validated methodology exists for evaluating their effects under realistic field conditions. UAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, it could provide the monitoring infrastructure through which MF treatment responses are, for the first time, systematically evaluated and validated under open-field conditions. To exploit this complementarity, however, a common evidential ground must first be established, identifying which crop physiological variables are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream systematic review of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency/nitrogen assimilation, above-ground biomass, leaf area index, and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R² = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The results reveal a complete absence of integration between the two research domains despite their strong biological and methodological compatibility. The proposed framework provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.