Multi-Band Accurate Soil Sensor for Simultaneous Moisture and Salinity Measurement: A Machine Learning-Enhanced Approach

This industry-driven paper presents the development of a novel multi-band soil sensor capable of simultaneously measuring volumetric water content (VWC) and potassium content, which serves as an indicator of soil salinity. The sensor operates by analyzing the soil’s electromagnetic (EM) properties. Unlike conventional Time-Domain Reflectometry (TDR) methods that rely on pulse signals and time-delay measurements, the proposed sensor leverages continuous-wave signals to capture phase differences and amplitude variations across a wide frequency range. By utilizing a frequency vector spanning from 1 to 10 MHz rather than a single operating frequency, the sensor achieves enhanced precision and reliability in data acquisition. A key feature of the proposed sensor is the integration of a machine learning model, which processes the collected raw data to accurately predict both VWC and salinity levels. The machine learning algorithm is trained to interpret complex signal variations, enabling the system to provide highly accurate and consistent measurements across different soil types and environmental conditions. Furthermore, the sensor includes a self-calibration circuit that compensates for external factors such as temperature fluctuations, soil composition variability, and other environmental influences, ensuring reliable performance across diverse agricultural and environmental monitoring applications. This multi-band sensing approach, combined with dual-measurement capability and advanced data processing techniques, offers a promising industry-grade solution for precision agriculture, irrigation management, and soil health monitoring. The proposed sensor system enables real-time, non-destructive, and cost-effective soil analysis, supporting sustainable agricultural practices by enabling data-driven decision-making.