RadMoist: Physics‐Guided Features and Hybrid Edge Inference for mmWave Soil‐Moisture Sensing in IoT Systems

Reliable and fine‐grained knowledge of near‐surface soil moisture is a foundation for precision irrigation, drought forecasting and hydrological modelling. However, existing probes, such as TDR and gravimetric sampling, remain labour‐intensive and sparse. This study presents RadMoist, a fully contactless framework that estimates volumetric water content with a single low‐cost 77 GHz FMCW radar (TI IWR1443BOOST, 4 GHz sweep and 3.8 cm range resolution). Beyond conventional surface amplitude and time‐of‐flight (ToF), RadMoist introduces three physics‐guided indices: Microangular backscatter slope (μABS) for angular roughness, phase coherence decorrelation index (PCDI) for subsecond stability and diffusion gradient index (DGI) for high‐band attenuation, yielding a five‐element feature vector. These cues contribute to a custom‐designed novel Hybrid‐MoistureNet, an ensemble that averages a 200‐tree XGBoost regressor with a compact three‐layer MLP, enabling sub‐millisecond inference on edge CPUs. The system was evaluated on loam, sandy‐loam and clay soils spanning 0%–40% volumetric water content. RadMoist achieved a mean absolute error of 6.9% VWC, improving on an amplitude + ToF baseline (9.8%) by 29% and matching laboratory TDR measurements within 2%. Performance remained stable under ± 8°C temperature drift and ± 10° sensor tilt demonstrating robustness to field conditions. Therefore, RadMoist offers a practical calibration‐light alternative for dense real‐time soil‐moisture mapping in precision agriculture and environmental monitoring.