Automated Robotic Moisture Monitoring in Agricultural Fields

Senthil, P.; Akila, I.S.

doi:10.1109/isitia.2018.8711348

Cited by 9 publications

(1 citation statement)

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“…to the trees, along the drip-lines. Previous research on SM sensing robots use larger platforms and very expensive sensor technology such as cosmic-ray sensors [11]- [13]. This limits how close the sensors can get to the region of interest and how frequently the surveys can be conducted.…”

Section: Introductionmentioning

confidence: 99%

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

Campbell¹,

Ye²,

Scudiero³

et al. 2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%