Citation: Balivada, S.; Grant, G.;
Zhang, X.; Ghosh, M.; Guha, S.;
Matamala, R. A Wireless
Underground Sensor Network Field
Pilot for Agriculture and Ecology:
Soil Moisture Mapping Using Signal
Attenuation. Sensors 2022, 22, 3913.
https://doi.org/10.3390/s22103913
Academic Editor: Paolo Bellavista
Received: 4 April 2022
Accepted: 19 May 2022
Published: 21 May 2022
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Article
A Wireless Underground Sensor Network Field Pilot for
Agriculture and Ecology: Soil Moisture Mapping Using
Signal Attenuation
Srinivasa Balivada
1,2
, Gregory Grant
1,2
, Xufeng Zhang
2,3,4,5
, Monisha Ghosh
1,6
, Supratik Guha
1,2
and Roser Matamala
4,7,
*
1
Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA;
balivadas@uchicago.edu (S.B.); gdgrant@uchicago.edu (G.G.); mghosh3@nd.edu (M.G.);
guha@uchicago.edu (S.G.)
2
Materials Science Division, Argonne National Laboratory, Lemont, IL 60439, USA;
xu.zhang@northeastern.edu
3
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA
4
The Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
5
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
6
Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
7
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
* Correspondence: matamala@anl.gov
Abstract:
Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor
data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In
this paper, we first show how WUSNs can operate reliably under field conditions year-round and at
the same time be used for determining and mapping soil conditions from the buried sensor nodes.
We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site
that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019,
enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil
electrical conductivity. Secondly, we present data collected over a nine-month period across three
seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using
various combinations of the received signal strength (RSSI) from each buried wireless node, above-
ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature
(AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and
precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST,
and RH as inputs was able to predict soil VWC with an R
2
of 0.82 for test datasets, with a Root Mean
Square Error of
±
0.012 (m
3
/m
3
). Hence, a combination of deep learning and other easily available
soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors
in WUSNs.
Keywords:
deep learning; RSSI; radio frequency attenuation; wireless underground sensor
network system
1. Introduction
Wireless Underground Sensor Networks (WUSNs) have been increasingly studied
over the past two decades for terrestrial, agricultural, and ecological applications [
1
–
9
],
including the demonstration of a fully buried, spatially distributed sensor node network
that will not disrupt agricultural and/or industrial processes [
10
–
17
]. Such sensor networks
have been explored as the backbone for geospatial internet-of-things (IoT) for agricultural
applications [
18
]. Data gathered from such networks, combined with data curation and
artificial intelligence-based analysis, is anticipated to be a significant component of future
digital farming and ecological practices provided challenges in cost and scalability are
met [
18
–
22
]. There are two important areas of WUSN applicability in agriculture and land
Sensors 2022, 22, 3913. https://doi.org/10.3390/s22103913 https://www.mdpi.com/journal/sensors
