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.
The effective and efficient use of irrigation water in agricultural production is vital to the long-term economic and environmental sustainability of irrigated farming operations. It is, therefore, important to develop and promote affordable and effective precision irrigation technologies for farmers to allow them to apply irrigation water when, where, and in the amount needed to maximize profits while protecting the environment. Over the years, a number of sensing technologies have been developed to help farmers properly schedule irrigation. These technologies usually rely on either sensing the weather conditions, sensing the plant itself, or sensing the soil.
Weather-based irrigation scheduling uses weather information and other ancillary inputs to model crop development and soil water status [
The use of plant sensors for irrigation scheduling, especially in arid areas, has focused on the use of infrared thermometers to sense canopy temperature [
Sensing the soil using soil moisture sensors is the other major technology that farmers can use to decide when to irrigate their crops and how much water to apply. A recent study with large-scale commercial corn farmers in Nebraska [
The development of low-cost and open-source microcontroller devices and software, and their ability to integrate wireless communication technologies, such as radio, cell-phone, and WiFi [
The system described in this paper was created to monitor soil water status from four depths using Watermark 200SS soil moisture sensors (Irrometer Company, Inc., Riverside, CA) (
matrix and the soil. The outer layer is a perforated stainless steel frame that provides rigidity and maintains the shape and physical integrity of the sensor. Two ABS plastic green caps are installed at each end of the sensor. The electrodes are connected to two AWG 20 lead wires that connect to the data acquisition system.
When installed in the soil, water is exchanged between the soil and the granular matrix until equilibrium is reached. Since water is an electrical conductor, the resistance between the electrodes is inversely related to soil moisture. Sampling the sensor involves powering it with an electrical current (AC rather than DC) and reading the electrical resistance output. The electrical resistance correlates to SWP (negative pressure), which is usually expressed in units of centibars (cb) or kilopascal (kPa) (cb = kPa). According to the manufacturer’s sensor specifications, the rated range of measurement of the Watermark 200SS sensors is from 0 to −239 kPa, although the normal usable range is from 0 to −200 kPa, where a reading around 0 kPa would indicate that the soil is at or near saturation and a reading at or near −200 would indicate a very dry soil with little or no plant available water. There are several types of commercial loggers that can automatically read the Watermark 200SS sensors at specified time intervals and store the collected data, and a device to manually read these sensors is also available (
The design of the data sampling and data communication system for four Watermark 200SS sensors consisted of a Coordinator and a number of End Nodes arranged in a star topology [
The End Nodes were created using the Adafruit Feather 32u4 RFM95 LoRa Radio (RFM9x) device (Adafruit Industries, New York, NY, adafruit.com), which combines an Arduino-based microcontroller with a Long Range (LoRa) packet radio transceiver. The microcontroller is based on the ATmega32u4 chip, clocked at 8MHz and using 3.3V logic. The radio transceiver can transmit or receive radio signals at a frequency of either 868 or 915 MHz, which can be specified in software. The Adafruit website (adafruit.com) claims that these radios can have a range of over 2 km (1.2 mi) line-of-sight using a wire quarter-wave antenna and that a range of around 20 km is possible by tweaking settings and using a directional antenna. Feather 0.1’’ Pitch Terminal Blocks (Adafruit Industries, New York, NY, adafruit.com) were soldered to the End Nodes to allow attaching wires from sensors and power supplies.
Since the output of the Watermark sensor is an electrical resistance, which cannot be directly measured by the microcontroller, a voltage divider circuit was created between the microcontroller and the sensor, as described by Fisher and Gould [
The Coordinator, on the other hand, was created by combining a Feather 32u4 RFM95 LoRa Radio (RFM9x) with a Feather M0 WiFi w/ATWINC1500 (Adafruit Industries, New York, NY, adafruit.com) (
The Watermark 200SS sensors were read with the microcontroller using a process similar to that described by Fisher and Gould (2012), except that the Feather microcontroller uses an excitation voltage of 3.2 VDC instead of 5 VDC. In short, each sensor was read by alternating the polarity of the DC voltage used to power the sensor between the two wires of the sensor. The sensor was first
Feather MO WINC1500 | Feather 32u4 RFM95 LoRA | Comment |
---|---|---|
GND | GND | |
SCL | SCL | Needs a pull-up resistor on the SCL line (4.7 kOhms connected to 3 V) |
SDA | SDA | Needs a pull-up resistor on the SDA line (4.7 kOhms connected to 3 V) |
Power (USB) | Power (USB) | The USB connection provides 5 V DC. |
powered (excited) by setting HIGH one of the two digital channels connected to the voltage divider while the other was set LOW. Then, a 1000 ms delay was allowed before taking a reading to allow for capacitance effects to stabilize. An analog reading was then taken on the analog channel connected to the sensor, using the analog to digital converter (ADC) to produce an integer output. The range of the integer output depends on the resolution (number of bits) of the ADC. Since the Feather device has a 10-bit ADC, the output will be in the range of 0 to 1023 (=2bits =210 =1024 values). Another reading was taken by reversing the polarity of excitation and the two readings were averaged. This process was repeated ten times, resulting in an average reading. The average reading was then converted to a voltage output (Vout), based on the input or excitation voltage (Vin = 3.2 V) as:
V o u t = r e a d i n g ∗ V i n 1023.0 (1)
Then, the resistance of the Watermark sensor (Rwm, KOhm) was calculated as:
R w m = [ r e s ∗ ( V i n − V o u t ) ] V o u t (2)
where, res = resistance used in the voltage divider (10 KOhm).
Fisher et al. [
S W P = − 4.093 + 3.213 ∗ R w m 1 − 0.009733 ∗ R w m − 0.01205 ∗ T s o i l (3)
where, Tsoil = soil temperature (˚C). However, this equation was originally developed with data in the range of only 0 to −80 kPa, which is less than half of the normal range of the Watermark 200SS sensors. Therefore, a calibration was developed in this study (see below) to convert Rwm to SWP that would be applicable to the whole response range of the Watermark 200SS sensors.
A lab test was also conducted to evaluate the voltage divider circuit and the software used to sample the Watermark 200SS sensor during a complete soil drying cycle. The test was conducted using and Arduino UNO and a data logging shield (Adafruit Industries, New York, NY, adafruit.com), which allowed recording the collected data in a SD card. The software for this test was developed using Equation 3 to calculate SWP. A Watermark 200SS sensor was submerged in water for 24 hours and was then installed in a 600 mL glass beaker filled with saturated soil. The soil was allowed to dry at room temperature for 21 days (Dec. 16, 2016 to Jan. 6, 2017) and the SWP was recorded every minute.
It was necessary to develop functions to convert the resistance output of the Watermark 200SS sensors into soil water potential, which required conducting sensor calibration experiments in the lab. The sensor calibrations were developed by comparing the outputs of the sensors taken using the microcontroller with their readings taken using the manual readouts (shown in
For calibrating the Watermark sensors, the four sensors were first totally submerged in water for 24 hours to allow for their granular matrix to saturate. The sensors were then installed vertically in a 600 mL glass beaker filled with saturated soil, making sure that there was good contact between the soil and the sensors (
temperature of 60˚C. The soil container was normally placed in the oven for about 30 minutes and was then allowed to cool at room temperature before the readings were taken. A fan was used to blow air to the soil sample and accelerate the cooling process. Since the calibration procedure took several days, readings were also taken at the end of the work day (around 5:00 pm) and at the start of the next work day (around 8:30 am), after allowing the soil sample to sit overnight at room temperature.
The idea behind the Internet-of-Things (IoT) is to have objects from our daily lives connected to the Internet, which allows these objects to periodically send data to the web, where they can then be accessible in real-time from remote locations [
For the soil moisture monitoring application, the ThingSpeak (http://www.thingspeak.com) IoT platform was used, mostly because it was free of charge and was set up to receive data from Arduino-based microcontrollers and from other microcontrollers such as Raspberry Pi, and BeagleBone Black. The ThingSpeak platform allows users to create a number of channels. Each channel contains data fields, location fields, and a status field. After a ThingSpeak channel is created, it can be used to write data to the channel, process and view the data with MATLAB® code, and react to the data with tweets and other alerts. The system assigns a unique code (key) to each channel. The Arduino-based microcontroller utilizes this key to direct the data to the specific ThingSpeak channel. The system is able to receive data as often as every 20 seconds, but our soil moisture monitoring system was programmed to update each channel every 20 minutes.
For testing the performance of the system in a real production field situation, the system was installed on February 1, 2017 to monitor soil moisture of a wheat field located at the Clemson University Edisto Research and Education Center near Blackville, SC. The field installation consisted of several End Nodes, each collecting data from different moisture sensor types, including the Watermark 200SS sensors. The Watermark 200SS sensors were installed at four depths (15, 30, 45, and 60 cm). Each Watermark 200SS sensor was glued to the end of a pvc pipe of appropriate length depending on installation depth, following the guidelines recommended by the manufacturer, in order to facilitate installation and eventual removal from the field at the end of the crop growing season [
The Coordinator was installed inside a waterproof enclosure attached to the outside wall of a field shed located around 200 m away from the End Node. The Coordinator was powered from a USB port, which provided a regulated 5 VDC output. The USB port was attached to a 120 VAC wall power outlet located inside the shed. The Coordinator connected to the Internet via WIFI provided by a mobile hotspot (Verizon Wireless Jetpack 6620L, 4G LTE) located inside the shed.
Results of initial lab testing to evaluate the voltage divider circuit and the software used to sample the Watermark 200SS sensors during a complete soil drying cycle are shown in
Item | Units | Unit Cost | Subtotal | Total |
---|---|---|---|---|
Coordinator: | ||||
Feather 32u4 RFM95 LoRA Radio (RFM9x) | 1 | $34.95 | $34.95 | |
Feather M0 WiFi w/ATWINC1500 | 1 | $34.95 | $34.95 | |
2.4 GHz Mini Flexible WiFi antenna with uFL connector | 1 | $2.50 | $2.50 | |
Enclosure | 1 | $18.50 | $18.50 | |
5 V USB port power supply | 1 | $5.95 | $5.95 | |
USB to Micro-USB cable | 1 | $4.00 | $4.00 | |
Feather female header kit (12-pin and 16-pin) | 2 | $0.95 | $1.90 | |
Circuit board (7 × 9 cm) | 1 | $1.05 | $1.05 | $103.80 |
Watermark 200SS End Node: | ||||
Feather 32u4 RFM95 LoRA Radio (RFM9x) | 1 | $34.95 | $34.95 | |
Voltage divider circuit for four Watermark sensors | 1 | $15.29 | $15.29 | |
Feather 0.1’’ Pitch Terminal Blocks | 1 | $5.95 | $5.95 | |
Battery (12 V, 7 Amp Sealed Lead Acid Battery) | 1 | $18.49 | $18.49 | |
Solar panel (5-Watt) | 1 | $19.00 | $19.00 | |
Solar charge controller/regulator | 1 | $10.99 | $10.99 | |
5 V Voltage regulator | 1 | $1.20 | $1.20 | |
Enclosure (Plastic, 7 × 12 × 6”) | 1 | $18.50 | $18.50 | $124.37 |
Watermark 200SS sensors | 3 | $35.00 | $105.00 | $105.00 |
er of the manual readout and loggers. In fact, the system recorded SWP values in the range of 0 to −39,779 kPa (full range not shown in
Results of calibration of the Watermark 200SS sensor are shown in
It was oftentimes observed that the Watermark 200SS readings taken a short time after taking the soil out of the oven (measured after allowing the soil sample to cool down to room temperature), tended to show wetter soil conditions compared to readings taken prior to putting the soil sample in the oven. This was contrary to what was expected, since water was supposed to evaporate from the soil sample, which would consequently decrease the soil water content. However, this unexpected behavior could be due to the fact that water evaporated from the top of the soil sample, which is likely to have moved water upwards buy capillary rise from the bottom of the container, which would be expected to be wetter than the top. Since the sensors were installed in the middle of the soil sample, the sensors would detect this temporary and localized increase in soil water content, although the average soil water content of the sample would actually be lower due to the evaporated water.
It was also noticed that allowing the soil to sit overnight after taking it out of the oven normally resulted in considerably lower soil water content (more negative SWP) compared to the readings taken shortly after taking the sample out of the oven. The behavior observed in this study can create problems and inaccuracies when trying to develop sensor calibrations by comparing sensor readings to gravimetric soil water content determined by weighing the soil sample. Another observation from
soil sample, which could be a significant problem when only one sensor is used to represent the water status of an entire agricultural field.
Each of the four sensors showed a good linear relationship (r2 > 0.97) between resistance and SWP in the range of SWP of 0 to −199 kPa. As stated earlier, the microcontroller was able to measure resistance values that would correspond to SWP values outside this range (represented by the open circles in
S W P = − 3.9585 * r e s i s t a n c e + 30.447 ( for S W P > − 199 ) (4)
And,
S W P = − 199 ( for S W P ≤ − 199 ) (5)
It is important to notice that the Watermark 1 sensor, which tended to be wetter than the other three sensors, also followed Equation (4), which seems to indicate that the sensor was not malfunctioning and that the observed higher (less negative) SWP values were in fact due to the sensor being wetter due to uneven water distribution within the soil sample. Equations (1) through (5) were, therefore, programmed into the microcontroller firmware to convert the readings taken with the Feather microcontroller to SWP.
The system reliably collected data every 20 minutes from a wheat field for more than three months (Feb. 1 to May 8, 2017). Sample results of the four Watermark 200SS sensors installed at four soil depths (15, 30, 45 and 60 cm) during April 22 to May 8, 2017 are shown in
in soil moisture conditions. The peaks in the curves indicate the quick response of the sensors to an increase in soil moisture due to rainfall events, followed by progressive drying of the soil profile, indicated by more negative soil water potential values. The smooth nature of the data shown in
Showing data for each sensor in a separate graph, using the default ThingSpeak settings, would hinder data interpretation. But, the ThingSpeak website provides access to Matlab, which allows writing scripts for creating customized displays and conducting additional analysis using the collected data. Therefore, a Matlab script was created to show the data from the four sensors in the same plot (
In this study, a system was developed to periodically collect data from four Watermark 200SS soil moisture sensors installed in a farm and automatically send the data to a website using radio and WiFi wireless communication. The system was developed using open-source electronics and software (Arduino-based devices) and using an open-source Internet of Things platform (ThingSpeak.com) to host and display the data. Prior to deploying the system in the field, laboratory tests were conducted to develop calibration functions to be able to use the Watermark 200SS sensors with the Arduino-based microcontroller. The system was installed in a production wheat field and data were collected successfully under real field conditions. The system described here can be built at an affordable price for growers. Potential future improvements for the system, however, could
include integrating the Coordinator into a printed circuit board and making the End Node more power efficient to conserve battery, so that a solar panel is not needed for field installation. Making the system more power efficient can be accomplished by taking advantage of the sleep-mode capabilities of the microcontroller or by using external hardware to only turn the system on when data collection is required. Our next step is to demonstrate and promote the use of the system to improve irrigation scheduling among commercial growers.
Technical contribution No. 6555 of the Clemson University Experiment Station. This material is based upon work supported by USDA/NIFA, under Projects Number SC-1700540, and SC-1700511, and USDA/NRCS under projects Number 69-3A75-13-88, and 69-4639-14-0010.
Mention of trade names does not imply endorsement of products by Clemson University to the exclusion of others that might be available.
Payero, J.O., 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. https://doi.org/10.4236/ait.2017.73005