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Near-ground sensing data, such as geospatial measurements of soil apparentelectrical conductivity (ECa), are used in precision agriculture to improvefarming practices and increase crop yield. Near-ground sensors provide valuableinformation, yet, the process of collecting, assessing, and interpretingmeasurements requires significant human labor. Automating parts of this processvia the use of mobile robots can help decrease labor burden, and increase theaccuracy and frequency of data collections, and overall increase the adoptionand use of ECa measurement technology. This paper introduces a roboticizedmeans to autonomously perform geospatial ECa measurements and map soil moisturecontent in micro-irrigated orchard systems. We retrofit a small wheeled mobilerobot with a small electromagnetic induction sensor by studying and taking intoconsideration the effect of the robot body to the sensor's readings, anddevelop a software stack to enable autonomous logging of geo-referencedmeasurements. The proposed roboticized ECa measurement method is evaluated bymapping a 50m x 30m field against the baseline of human-conducted measurementsobtained by walking the sensor in the same field and following the same path.Experimental testing reveals that our approach yields roboticized measurementscomparable to human-conducted ones, despite the robot's small form factor.

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