A Portable Agricultural Robot for Continuous Apparent Soil ElectricalConductivity Measurements to Improve Irrigation Practices

TL;DR

This paper presents a small autonomous robot equipped with an electromagnetic sensor that efficiently and accurately measures soil electrical conductivity, aiding precision agriculture by automating data collection and mapping soil moisture.

Contribution

It introduces a novel robotic system for autonomous geospatial soil electrical conductivity measurements, considering robot-body effects on sensor readings, and demonstrates comparable accuracy to human measurements.

Findings

Robotic measurements are comparable to human data.

The robot effectively maps soil moisture content.

The system reduces labor and increases data collection frequency.

Abstract

Near-ground sensing data, such as geospatial measurements of soil apparent electrical conductivity (ECa), are used in precision agriculture to improve farming practices and increase crop yield. Near-ground sensors provide valuable information, yet, the process of collecting, assessing, and interpreting measurements requires significant human labor. Automating parts of this process via the use of mobile robots can help decrease labor burden, and increase the accuracy and frequency of data collections, and overall increase the adoption and use of ECa measurement technology. This paper introduces a roboticized means to autonomously perform geospatial ECa measurements and map soil moisture content in micro-irrigated orchard systems. We retrofit a small wheeled mobile robot with a small electromagnetic induction sensor by studying and taking into consideration the effect of the robot body to the sensor's readings, and develop a software stack to enable autonomous logging of geo-referenced measurements. The proposed roboticized ECa measurement method is evaluated by mapping a 50m x 30m field against the baseline of human-conducted measurements obtained by walking the sensor in the same field and following the same path. Experimental testing reveals that our approach yields roboticized measurements comparable to human-conducted ones, despite the robot's small form factor.