The widespread adoption of soil sensors in precision farming, measuring moisture, temperature, and electrical conductivity, aims to optimize irrigation and fertilization practices. However, sensor effectiveness hinges on accurate calibration considering variables like texture class, temperature, and soil moisture. This study presents a benchmark calibration method for soil moisture and electrical conductivity sensors (SMEC300), focusing on three common agricultural soil textures: sand, silty loam, and silt. Sensor performance across varied levels of electrical conductivity (EC), volumetric water content (VWC), and temperature is evaluated. A calibration equation is derived to determine VWC and EC, with assessments based on coefficient of determination, residual sum of squares, and Pearson correlation coefficient. Results show linear sensor response to salinity changes in silty clay and silt, while sand exhibits higher variability. Temperature impacts soil moisture readings up to 25 °C, beyond which fluctuations and outliers increase. Performance variations are observed among soil types, with sand and silt outperforming silty clay within a 0–45 °C temperature range. EC calibration in free water indicates rising standard deviation with increasing electrical conductivity, with minimal outliers in silt, followed by silty clay and sand. Additionally, EC rises linearly with temperature up to 25 °C for 50% saturation. Error bands and confidence intervals are overlaid to determine the optimal number of sensors for field use, suggesting a 7-sensor combination system to minimize measurement errors across temperature and salinity variations. The calibration procedure highlights that sensor outputs are simultaneously influenced by salinity, followed by temperature and VWC.