Sensor network for measuring soil moisture

Described herein are embodiments of a system comprising an underground sensor network that measures soil moisture by quantifying how changes in soil water content affect the attenuation of radio signals between buried sensor nodes. Also disclosed herein are methods of using the described system for measuring soil moisture and using the measurement for various control aspects including for example controlling irrigation in agricultural and urban settings.

BACKGROUND

Soil water content affects almost every ecological, agricultural, and hydrological process at the land-atmosphere interface. Soil moisture, especially near the surface, varies in space and time in response to a long list of biophysical factors making it difficult to measure and model. Water managers and other stakeholders could use real-time soil moisture data if it were readily available. Unfortunately, sensors that provide automated, continuous soil water measurements and connect to the internet (i.e., the Cloud) are expensive and complex. Commercial sensors and dataloggers cannot be economically deployed in large enough numbers to make them useful for applied water management.

Furthermore, efficient irrigation (timing and amount) hinges on knowing the current soil moisture status in the soil. However, running wires and installing multiple radio towers within a field or urban landscape is often impractical.

Recent developments associated with3D printing, electronics, and Internet-of-things connectivity have “opened the door” for a new generation of real-time soil moisture measurement technology. Underground sensor networks offer the advantage of being essentially “invisible” while providing highly granular soil data. Such a system would be especially useful in the automated management of irrigation systems associated with precision agriculture. The technology could also be a boon for controlling urban irrigation systems (turf and landscape irrigation, golf courses, etc.)

Therefore, what is needed is systems and methods that overcome challenges in the art, some of which are described above.

SUMMARY

Described herein are embodiments of a system comprising an underground sensor network that measures soil moisture by quantifying how changes in soil water content affect the attenuation of radio signals and the quality of the network radio link between buried sensor nodes. Also disclosed herein are methods of using the described system for measuring soil moisture and using the measurement for various control aspects including for example controlling irrigation in agricultural and urban settings.

A network comprised of a plurality of underground sensor nodes gather and provide data to an aboveground node (or gateway). Generally, data transmission among underground nodes and to the gateway nodes is accomplished wirelessly, though in some instances there may be wires between one or more of the underground sensor nodes and the gateway. As used herein, “wires” includes electrically conductive elements (insulated or non-insulated) as well as fiber optic cables. In some instances, a “master” underground node provides data to the gateway, while in other instances the gateway receives data from each or from several of the underground sensor nodes. Generally, each of the plurality of sensor nodes are transceivers, though in some instances some of the underground sensor nodes are transmitters only and others are receivers only. The data provided to the gateway comprises information about attenuation of signals between the signal nodes and the quality of the wireless network connection among nodes. The quality of the network connection is quantified by sending100sof date packets between nodes and measuring packet reception ratio, link quality indicator, and latency. The attenuation data is correlated with soil moisture content. Network link quality is also affected by soil moisture content, providing additional information on soil moisture status.

Each underground sensor node is small (e.g., domino-sized) and has an onboard power source such as a battery, which can last for up to 10 years. In some instances, the gateway receives data from the underground network of sensor nodes and routes the data to a cloud computing network for analysis. A typical underground sensor node is comprised of a microprocessor, a radio transceiver, an antenna, and a power source (e.g., a battery). On a periodic basis (e.g., every hour, every two hours, every 30 minutes, etc.), the aboveground gateway polls the underground sensor network and obtains measurements related to attenuation of signals between the underground sensor nodes (e.g., relative received signal strength (RSSI) and/or packet reception rate (PRR) between nodes). The signal attenuation information (e.g., RSSI and/or PRR information) between nodes is used to approximate soil moisture along the path between nodes (i.e., how water in the soil attenuates radio signal strength). In some instances, one or more of the plurality of underground sensor nodes may further comprise a temperature sensor and/or a soil moisture sensor, and the polled data may include one or more of a battery status of each underground sensor node and measurements of soil moisture and temperature having the on-board sensors. Data from the soil moisture sensors is used for data quality assurance (i.e., backup data) and system calibration as soil moisture is generally determined by the attenuation of signals along vertical or horizontal radio signal propagation paths below the surface.

The gateway and/or the cloud computing network receives data from the underground sensor nodes and determines signal attenuation between the sensor nodes. An algorithm executing on a processor on the gateway and/or in the cloud computing network correlates the signal attenuation with soil moisture content. The determined soil moisture content can then be used to control other systems. For example, an irrigation system may be turned on or off or water flow adjusted based on the determined soil moisture content. Artificial intelligence (AI) or machine learning algorithms can be used in the control of these other systems.

DETAILED DESCRIPTION

FIG. 1Ais an exemplary overview illustration of a system for determining soil moisture content. As shown inFIG. 1A, a plurality of underground sensor nodes102form a network. The underground sensor nodes102are typically buried from 10 to 100 cm, depending upon the application. For example, on a golf course the sensor nodes102may be buried only a few centimeters in depth—deep enough to avoid aeration spikes, while in a farm field the sensor nodes102may be buried a depth of 20 cm or more so that they are not dug up when the field is plowed. Sensor nodes102are typically spaced apart by one to two meters, though in some instances the plurality of sensor nodes can be spaced up to 25 meters from one another. In some instances, one or more sensor nodes102may be located on top or above the ground level so that attenuation between an underground sensor and a sensor node at or above ground level can be measured. Generally, each underground sensor node102comprises a transceiver—it both transmits and receives signals. A non-limiting example of a transceiver that may be used is RFM69HCW or RFM96LoRa by HopeRF Electronic (Shenzhen, China). In some instances, though, some of the underground sensor nodes102may only be transmitters, while others are only receivers. Each sensor node102has an onboard power source, typically a battery. A non-limiting example of a battery that can be used is a 600 mAH, 3.7V, Lithium ion polymer battery. Generally, each sensor node is encapsulated to prevent infiltration of water and dirt and the ensuing degradation of the components.

Some or all of the sensor nodes102transmit signals while some or all of the sensor nodes102receive the signals. Signals typically have a frequency from 200 MHz to 1000 MHz. For example, in some applications the signals are 433 MHz. In some other applications the signals may be 915 MHz. Each sensor node102that is transmitting a signal transmits it at a defined power level. For example, a typical power level may be 20 dB, though other power levels can be used. Sensor nodes102that receive a transmitted signal record the power level of the received signal. This information (the power level of the transmitted signal and the power level of the received signal) is used to determine attenuation of the signal. Attenuation is typically greater in soil with a higher moisture content and lesser in soil that is drier.

Data from the sensor nodes102is transmitted to an aboveground gateway104. (which can then be accessed by remote computers via any wired or wireless networks, including the Internet, cellular, or satellite). In some instances, the data is transmitted wirelessly from all or some of the sensor nodes102to the gateway104, in other instances there is a wire between the gateway104and all or some of the sensor nodes102(seeFIG. 1B), while in other instances there may be a combination of wired and wireless connections between the sensor nodes102and the gateway104. Generally, each sensor node102spends most of the time in sleep mode to conserve power, then wakes at a pre-programmed interval as controlled by an on-board hardware watchdog timer. A unique method keeps all the underground nodes synchronized (i.e., waking and sleeping on the same schedule) without using real time clocks. This is accomplished by interrupting power to the onboard watchdog timer chip using a MOSFET when the gateway node104seeds a command to sleep. The gateway node104, spends most of its time in listening mode. However, once the gateway104detects that the underground nodes are awake, it initiates a unique polling routine that collects data from all the sensor nodes102via the master sensor nodes106. Once data are collected, the gateway104instructs all the underground nodes (sensor nodes102and master sensor nodes106) to enter sleep mode. The period between polls may be any time period, for example one hour, two hours, two and one-half hours, four hours, 10 hours, etc. As stated previously, the sensor node102is in sleep mode between polls in order to extend battery life. The sensor node102is programmed to “wake” itself on a periodic basis, where it is recognized by the gateway104. The gateway104then initiates a measurement and data transfer protocol between all 102 and 106 nodes. In some instances, a sensor node102buffers the sensor data from adjacent nodes to local memory or flash memory until it is transmitted to the gateway104and/or a “master” sensor node106.

In some instances, the sensor nodes102transmit their data to a “master” sensor node106, and the master sensor node106transmits the data to the gateway104, while in other instances each sensor node102may transmit its data directly to the gateway104. In some instances, the “master” sensor node106may be connected to the gateway104by a wire. In other instances, the “master” sensor node106transmits data wirelessly to the gateway104. Sensor nodes102may be configured to dynamically self-organize and form an adaptive mesh network (where one or more sensor nodes may act as “master” sensor nodes106) that allows them to communicate with each other and relay data to the gateway104. All sensor nodes102and “master” sensor nodes106are powered using, for example, batteries or other self-contained energy sources. Sensor nodes102can be programmed to perform a wide range of tasks including sampling, storage, processing, and communication of sensor data. Generally, a sensor node102transmits data to the “master” sensor node106and/or the gateway104using the same transmitter (and frequency) that it transmits signals to other sensor nodes102.

Data is transferred from the gateway104to a computing network. Typically, the computing network comprises a cloud computing network108. Generally, the gateway104will have a receiver and/or transceiver for receiving data from the plurality of sensor nodes102, and a wireless radio for transmitting the data from the gateway104to the cloud computing network108. The radio may comprise, for example, a WiFi (IEEE 802.11n), Bluetooth, cellular, or satellite transmitter. The gateway104typically has its own power source, which may be batteries and/or solar panels. In some instances, the solar panels can be used to recharge the batteries of the gateway104. In some instances, the gateway104can be, for example, a microcomputer used for both control and monitoring of the sensor array as well as for data storage and analysis. The gateway104can be positioned so that it does not interfere with above-ground operations such as those of a golf course, farm, or other application. For example, the gateway104may be located up to 60 meters from the plurality of sensor nodes.FIG. 2illustrates relative signal strength (RSSI) of a 433 MHz signal as affected by distance between a 25-cm-deep buried sensor node102and an aboveground gateway104.

Returning toFIG. 1B, an illustration of an embodiment of a sensor node102is shown. A typical underground sensor node102is comprised of a microprocessor, a radio transmitter and/or a radio receiver, or a transceiver, an antenna, and a power source (e.g., a battery). On a periodic basis (e.g., every hour, every two hours, every 30 minutes, etc.), the aboveground gateway polls the underground sensor network and obtains measurements related to attenuation of signals between the underground sensor nodes (e.g., RSSI and/or PRR between nodes). The signal attenuation information (e.g., RSSI and/or PRR information) between nodes is used to approximate soil moisture along the path between nodes (i.e., how water in the soil attenuates radio signal strength). In some instances, one or more of the plurality of underground sensor nodes may further comprise a temperature sensor and/or a soil moisture sensor, and the data may include one or more of a battery status of each underground sensor node and measurements of soil moisture and temperature having the on-board sensors. In some embodiments, the soil moisture sensor comprises a capacitive sensor. Data from the soil moisture sensor is used for data quality assurance (i.e., backup data), as soil moisture is generally determined by the attenuation of signals along vertical or horizontal radio signal propagation paths below the surface. Generally, the soil moisture sensor provides small scale measurements close to the sensor while radio attenuation determines soil moisture content at larger scales governed by the installation depth and the horizontal distance among sensor nodes102. A soil temperature sensor provides useful additional information to a user of the network. For example, soil temperature can be used to optimize the date of planting or predict the emergence of pests and pathogens.

As noted above, the data transmitted from the underground sensor node102to the gateway104includes at least information about the received signal strength (assuming that all sensor nodes transmit power at the same power level). For example, the data may include RSSI information or PRR information, or RSSI and PRR information. Optionally, the data may include additional information such as information about the temperature of the soil and/or the measured moisture content of the soil at the sensor location. In some instances, the data may include an indication of the battery life of the sensor node102(i.e., a battery status). The sensor nodes102send packets of information to master sensor nodes106and/or gateway104. Each packet may contain multiple variables, (node identifier, RSSI, PRR, battery status, temperature, etc.). If the node contains the optional soil moisture sensor (seeFIG. 1C), a local measurement of soil moisture is also sent in the data packet.FIG. 3is an image of a typical sensor node102.

Some or all of the data received by the gateway104from the plurality of underground sensor nodes102is transmitted to a computing network. Generally, this will be a cloud computing network108. The cloud computing network108analyzes the data and makes determinations about the moisture content of the soil in the area where the plurality of sensor nodes102are located. For example, the cloud computing network108executes algorithms that correlates the attenuation information with soil moisture content. A non-limiting example of such correlation is shown inFIG. 4, which illustrates RSSI graphed against volumetric soil water content. In some instances, the cloud computing network108may employ AI and adaptive learning to provide an input to a control system110. For a non-limiting example, an embodiment of the disclosed soil moisture content system may be used to control an irrigation system. Though the measured moisture content may be low, the cloud computing network108may receive an input from a weather monitoring station112, so the cloud computing network108may train its AI such that the irrigation system is not employed when the weather indicates rain, or it may reduce the amount of water used during irrigation to accommodate the expected rainfall. Other non-limiting examples of the application of AI include using weather forecast information to estimate expected water use (i.e., evapotranspiration) using well established formula (e.g., American Society of Civil Engineers Standardized Reference Evapotranspiration Equation). Thus, forecasts of precipitation and expected consumptive use can be combined with the soil moisture information to improve the timing and amount of irrigation. As the system operates over time, machine learning algorithms obtain feedback from the soil moisture network and become better at scheduling irrigations on a site-specific basis.

FIG. 5illustrates an exemplary computer that may comprise all or a portion of a sensor node102, a gateway104, a “master” sensor node106, a cloud computing network108, and/or a control system110. Conversely, any portion or portions of the computer illustrated inFIG. 5may comprise all or a portion of a sensor node102, a gateway104, a “master” sensor node106, a cloud computing network108, and/or a control system110. As used herein, “computer” may include a plurality of computers. The computers may include one or more hardware components such as, for example, a processor1021, a random-access memory (RAM) module1022, a read-only memory (ROM) module1023, a storage1024, a database1025, one or more input/output (I/O) devices1026, and an interface1027. Alternatively, and/or additionally, the computer may include one or more software components such as, for example, a computer-readable medium including computer executable instructions for performing a method associated with the exemplary embodiments such as, for example, an algorithm for determining signal attenuation and correlating the signal attenuation with soil moisture content. It is contemplated that one or more of the hardware components listed above may be implemented using software. For example, storage1024may include a software partition associated with one or more other hardware components. It is understood that the components listed above are exemplary only and not intended to be limiting.

Processor1021may include one or more processors, each configured to execute instructions and process data to perform one or more functions associated with a computer for controlling a system (e.g., an irrigation system) and/or receiving and/or processing and/or transmitting data associated with a network of sensor nodes used to measure soil moisture content. Processor1021may be communicatively coupled to RAM1022, ROM1023, storage1024, database1025, I/O devices1026, and interface1027. Processor1021may be configured to execute sequences of computer program instructions to perform various processes. The computer program instructions may be loaded into RAM1022for execution by processor1021.

RAM1022and ROM1023may each include one or more devices for storing information associated with operation of processor1021. For example, ROM1023may include a memory device configured to access and store information associated with the computer, including information for identifying, initializing, and monitoring the operation of one or more components and subsystems. RAM1022may include a memory device for storing data associated with one or more operations of processor1021. For example, ROM1023may load instructions into RAM1022for execution by processor1021.

Storage1024may include any type of mass storage device configured to store information that processor1021may need to perform processes consistent with the disclosed embodiments. For example, storage1024may include one or more magnetic and/or optical disk devices, such as hard drives, CD-ROMs, DVD-ROMs, or any other type of mass media device.

Database1025may include one or more software and/or hardware components that cooperate to store, organize, sort, filter, and/or arrange data used by the computer and/or processor1021. For example, database1025may store data related to the soil moisture content correlated with signal attenuation. The database may also contain data and instructions associated with computer-executable instructions for controlling a system (e.g., an irrigation system) and/or receiving and/or processing and/or transmitting data associated with a network of sensor nodes used to measure soil moisture content. It is contemplated that database1025may store additional and/or different information than that listed above.

I/O devices1026may include one or more components configured to communicate information with a user associated with computer. For example, I/O devices may include a console with an integrated keyboard and mouse to allow a user to maintain a database of digital images, results of the analysis of the digital images, metrics, and the like. I/O devices1026may also include a display including a graphical user interface (GUI) for outputting information on a monitor. I/O devices1026may also include peripheral devices such as, for example, a printer, a user-accessible disk drive (e.g., a USB port, a floppy, CD-ROM, or DVD-ROM drive, etc.) to allow a user to input data stored on a portable media device, a microphone, a speaker system, or any other suitable type of interface device.

Interface1027may include one or more components configured to transmit and receive data via a communication network, such as the Internet, a local area network, a workstation peer-to-peer network, a direct link network, a wireless network, or any other suitable communication platform. For example, interface1027may include one or more modulators, demodulators, multiplexers, demultiplexers, network communication devices, wireless devices, antennas, modems, radios, receivers, transmitters, transceivers, and any other type of device configured to enable data communication via a wired or wireless communication network.

Throughout this application, various publications may be referenced. The disclosures of these publications in their entireties are hereby incorporated by reference into this application in order to more fully describe the state of the art to which the methods and systems pertain.