Multi-Band Moisture and Salinity Sensor (MMSS): A Cost-Effective and Accurate ML-Driven Solution for Precision Soil Monitoring

This industry-driven paper introduces a novel multi-frequency soil sensor for precision agriculture applications. The sensor simultaneously measures Volumetric Water Content (VWC) and salinity by leveraging the soil’s electrical properties. Unlike traditional Time Domain Reflectometry (TDR) sensors, which rely on pulse signals and time-delay measurements, the proposed sensor utilizes continuous waves, phase shifts, and amplitude variations to enhance accuracy and reduce system complexity. Operating simultaneously at frequencies of 70 MHz, 140 MHz, and 210 MHz , the multi-frequency approach mitigates the errors inherent in single-frequency measurements, ensuring improved precision even in heterogeneous soils. A cost-effective frequency generation method utilizing a single voltage-controlled oscillator (VCO) and its harmonics was implemented, significantly reducing hardware requirements while maintaining high performance. Additionally, machine learning models, trained on a dedicated calibration dataset, were used to accurately interpret raw sensor data and predict both VWC and salinity values. Machine learning models, including a Neural Network (NN) and Support Vector Regression (SVR) with a radial basis function (RBF) kernel, were developed and compared to estimate soil volumetric water content and salinity from phase and amplitude data. Experimental results demonstrated the proposed MMSS sensor achieves high accuracy in predicting soil parameters, with mean absolute error (MAE) of 0.4% for VWC and 0.05 g/kg for potassium concentration, outperforming existing soil moisture sensors, and demonstrating robust performance for precision agriculture applications and environmental monitoring. Its accuracy, scalability, and cost-effectiveness make it a valuable tool for optimizing resource usage and enhancing crop yields.