Robot-assisted Soil Apparent Electrical Conductivity Measurements in Orchards

TL;DR

This paper introduces a robot-assisted method for measuring soil electrical conductivity in orchards, offering a scalable, cost-effective, and accurate alternative to traditional labor-intensive techniques, with extensive experimental validation.

Contribution

The study presents a customizable robotic platform with an EMI sensor, simulation software for terrain assessment, and demonstrates high accuracy in soil ECa measurements compared to manual methods.

Findings

Achieved over 90% Pearson correlation with manual measurements.

Validated the robot's traversability and sensor placement across multiple fields.

Enabled semi-autonomous, on-demand soil ECa measurements in orchard environments.

Abstract

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 ECa maps generated by kriging interpolation. The proposed robotic solution supports autonomous behavior development in the field since it utilizes the ROS navigation stack along with the RTK GNSS positioning data and features various ranging sensors.