In all-optical magnetic field sensing using nitrogen-vacancy-center-rich diamonds, an ambiguity in the range of 0–8 mT can be observed. We propose a way to resolve this ambiguity using the magnetic-field-dependent fluorescence lifetime. We therefore recorded the frequency response of the fluorescence upon modulation of the excitation intensity in a frequency range of 1–$100\mathrm{MHz}$. The magnetic-field-dependent decay dynamics led to different response characteristics for magnetic fields below and above $3\mathrm{mT}$, allowing us to resolve the ambiguity. We used a physics-based model function to extract fit parameters, which we used for regression, and compared it to an alternative approach purely based on an artificial neural network.

This paper presents the application of a compact and fully integrated LED quantum sensor based on the NV centers in diamond for current measurement in a busbar. The magnetic field measurements from the sensor are directly compared with measurements from a numerical simulation, eliminating the need for calibration. The sensor setup achieves an accuracy of 0.28% in the measurement range of 0–30 $\mathrm{A}$ DC. The integration of advanced quantum sensing technology with practical current measurement demonstrates the potential of this sensor for applications in electrical and distribution networks.

Negatively charged nitrogen–vacancy (NV) centers in diamond are promising platforms for magnetic field sensing under ambient conditions, yet practical deployment is often limited by hardware complexity, power consumption, and computational cost. All-optical approaches based on fluorescence lifetime measurements avoid microwave excitation and enable simpler, potentially more scalable sensor designs, but the nonlinear response to magnetic and thermal fields requires advanced modeling. In this study, a systematic comparison of 18 regression algorithms—including linear models, ensemble methods, support vector machines, and symbolic regression—was conducted for predicting magnetic field strength from NV fluorescence lifetime data. A dataset of 22 758 measurements was employed, and models were assessed in terms of both predictive accuracy and computational footprint (training time, model complexity, and inference latency). The results indicate that ensemble techniques such as LightGBM and XGBoost achieve near-perfect accuracy while maintaining subsecond inference times, making them suitable for real-time embedded deployment. In contrast, symbolic regression provides negligible inference cost but suffers from significantly lower accuracy. These findings offer quantitative guidance for the selection of machine learning models that balance precision and efficiency, paving the way toward compact, low-power, all-optical NV magnetometers for resource-constrained platforms.