High-Resolution CSRR-Based Microwave Sensor for Soil Moisture Content Monitoring.

This work presents a compact square complementary split-ring resonator (CSRR) microwave sensor, combined with a machine-learning-based calibration strategy, to achieve superior texture-aware soil moisture quantification. Implemented on a Rogers RO3010 substrate with a 20 × 30 mm2 footprint and operating near 1.3 GHz, the sensor exploits shifts in resonance/notch frequency and insertion loss (S21) to probe both the real and imaginary components of the soil's complex permittivity. Full-wave 3D electromagnetic simulations guided optimisation of the CSRR topology and T-shaped microstrip feedline, yielding strong field confinement, high quality factor, and high Frequency Detection Resolution (FDR). Experiments on sand and loam across 0-30% and 0-40% moisture content ranges, respectively, demonstrate FDR values of 6.09 MHz (sand) and 6.86 MHz (loam), enabling discrimination of subtle permittivity changes. Several calibration strategies are developed and compared for complex permittivity extraction from measured S-parameters: linear and polynomial regression, a multivariable least-squares sensitivity-matrix model, and a delta-referenced multilayer perceptron (MLP) with z-score standardization. While polynomial and least-squares models significantly outperform linear regression (R2 > 0.997), the MLP combined with the optimized CSRR architecture delivers the best performance, achieving near-ideal accuracy (R2 ≈ 1, MAE < 0.001, RMSE < 0.001) for both soil types. These results demonstrate that the synergy between the novel CSRR sensor design and data-driven MLP calibration enables high-resolution, robust, and field-deployable soil moisture sensing, offering a compelling solution for next-generation agricultural and geotechnical monitoring systems.