A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-05, Vol.22 (10), p.3913
• Main Authors: Balivada, Srinivasa, Grant, Gregory, Zhang, Xufeng, Ghosh, Monisha, Guha, Supratik, Matamala, Roser
• Format: Article
• Language: English
• Subjects: Agriculture; Air temperature; Algorithms; Artificial intelligence; Deep learning; Dielectric properties; Ecology; Electrical resistivity; Farms; Internet of Things; Laboratories; Machine learning; Moisture content; Neural networks; Nodes; OTHER INSTRUMENTATION; Power; radio frequency attenuation; Relative humidity; RSSI; Sensors; Signal strength; Soil conditions; Soil mapping; Soil moisture; Soil sciences; Soil temperature; Soil water; User interface; Wireless networks; wireless underground sensor network system
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22103913

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 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m /m ). 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.