Soil apparent electrical conductivity (ECa) is a vital metric in Precision Agriculture and Smart Farming, as it is used for optimal water content management, geological mapping, and yield prediction. Several existing methods seeking to estimate soil electrical conductivity are available, including physical soil sampling, ground sensor installation and monitoring, and the use of sensors that can obtain proximal ECa estimates. However, such methods can be either very laborious and/or too costly for practical use over larger field canopies. Robot-assisted ECa measurements, in contrast, may offer a scalable and cost-effective solution. In this work, we present one such solution that involves a ground mobile robot equipped with a customized and adjustable platform to hold an Electromagnetic Induction (EMI) sensor to perform semi-autonomous and on-demand ECa measurements under various field conditions. The platform is designed to be easily re-configurable in terms of sensor placement; results from testing for traversability and robot-to-sensor interference across multiple case studies help establish appropriate tradeoffs for sensor placement. Further, a developed simulation software package enables rapid and accessible estimation of terrain traversability in relation to desired EMI sensor placement. Extensive experimental evaluation across different fields demonstrates that the obtained robot-assisted ECa measurements are of high linearity compared with the ground truth (data collected manually by a handheld EMI sensor) by scoring more than 90% in Pearson correlation coefficient in both plot measurements and estimated soil apparent electrical conductivity maps generated by kriging interpolation. The proposed robotic solution supports autonomous behavior development in the field since it utilizes the Robot Operating System (ROS) navigation stack along with the Real-Time Kinematic (RTK) GNSS positioning data and features various ranging sensors. Agricultural geophysics employs non-invasive sensing techniques to characterize soil spatial variability and provide valuable insights into soil-plant-management relationships [1], [2]. Specifically, geospatial information on soil characteristics can indicate optimal cultivation approaches and may provide an estimate of expected yields [3], [4]. As such, it has been used widely across applications such as in agriculture, water management, geological mapping, and engineering surveys [5]-[7].