Author(s)

José O. Payero^1*, Ali Mirzakhani-Nafchi², Ahmad Khalilian², Xin Qiao¹, Rebecca Davis¹

¹Edisto Research and Education Center, Clemson University, Blackville, SC, USA.
²Department of Agricultural Sciences, Clemson University, Clemson, SC, USA.

Soil moisture monitoring is one of the methods that farmers can use for irrigation scheduling. Many sensor types and data logging systems have been developed for this purpose over the years, but their widespread adoption in practical irrigation scheduling is still limited due to a variety of factors. Important factors limiting adoption of soil moisture sensing technology by farmers include high cost and difficulties in timely data collection and interpretation. Recent developments in open source microcontrollers (such as Arduino), wireless communication, and Internet-of-Things (IoT) technologies offer opportunities for reducing cost and facilitating timely data collection, visualization, and interpretation for farmers. Therefore, the objective of this study was to develop and test a low-cost IoT system for soil moisture monitoring using Watermark 200SS sensors. The system uses Arduino-based microcontrollers and data from the field sensors (End Nodes) are communicated wirelessly using LoRa radios to a receiver (Coordinator), which connects to the Internet via WiFi and sends the data to an open-source website (ThingSpeak.com) where the data can be visualized and further analyzed using Matlab. The system was successfully tested under field conditions by installing Watermark sensors at four depths in a wheat field. The system described here could contribute to widespread adoption of easy-to-use and affordable moisture sensing technologies among farmers.

Irrigation Scheduling, Automation, Sensors, Soil Moisture, Internet-of-Things, Arduino, Watermark Sensors, Calibration

Payero, J. , Mirzakhani-Nafchi, A. , Khalilian, A. , Qiao, X. and Davis, R. (2017) Development of a Low-Cost Internet-of-Things (IoT) System for Monitoring Soil Water Potential Using Watermark 200SS Sensors. Advances in Internet of Things, 7, 71-86. doi: 10.4236/ait.2017.73005.