Currently available soil volumetric water content (VWC) sensors have several drawbacks that pose certain challenges for implementation on large scale for farms. Such issues include cost, scalability, maintenance, wires running through fields, and single-spot resolution. The development of a passive soil moisture sensing system utilizing Radio Frequency Identification (RFID) would allay many of these issues. The type of passive RFID tags discussed in this paper currently cost between 8 to 15 cents retail per tag when purchased in bulk. An incredibly cheap, scalable, low-maintenance, wireless, high-resolution system for sensing soil moisture would be possible if such tags were introduced into the agricultural world. This paper discusses both the use cases as well as examines one implementation of the tags. In 2015, RFID tag manufacturer SmarTrac started selling RFID moisture sensing tags for use in the automotive industry to detect leaks during quality assurance. We place those tags in soil at a depth of 4 inches and compared the moisture levels sensed by the RFID tags with the relative permittivity (εr) of the soil as measured by an industry-standard probe. Using an equation derived by Topp et al, we converted to VWC. We tested this over a wide range of moisture conditions and found a statistically significant, correlational relationship between the sensor values from the RFID tags and the probe’s measurement of εr. We also identified a possible function for mapping vales from the RFID tag to the probe bounded by a reasonable margin of error.

Advancements in sensing technology have sparked a new age of data acquisition and transmission that continue to change the way we understand the world around us. In earth science, we often must move and store tremendous amounts of data from remote locations. Present options are limited to costly propriety devices, which are rigid in structure and have numerous expenses associated with their use. The solution developed in the Openly Published Environmental Sensing Lab (OPEnS) at Oregon State University, was to employ a new methodology using low-power, open-source hardware, and software, to achieve near-real-time data logging from the field to the web. This new approach simultaneously lowers the cost of experimentation and data collection and breaks down traditional technical barriers. Data can be collected remotely from nearly anywhere on Earth using a decentralized OPEnS Hub which can utilize a host of low bandwidth transmission protocols and modes of communication, such as: 900 MHz Long Range Radio (LoRa) with a transmission distance of up to 25 km, the Global System for Mobile communications (GSM) using well established cell network infrastructure, Wi-Fi for high bandwidth applications, and Ethernet where LAN connections are available. It is notable that LoRa technology is still developing and has been expanded to transmit to an ever-growing constellation of satellites, making this technology truly global in its applicability. The OPEnS-Hub is capable of mesh networking with other nodes and will parse and back up the data to an onboard microSD card. By first exploiting a free open-sourced Application Programming Interface (API), PushingBox, acting as a data broker, and secondly, a customized Google App script, the OPEnS-Hub was able to achieve a dynamic, low latency portal connecting to google sheets. These methods working in tandem allowed for near real-time data logging of over a dozen devices each with unique sensor suites to form valuable time series data. This poster details our methods and evaluates the application and development of PushingBox’s API, Google App Script, Adafruit’s open-hardware Feather development boards, the Hypertext Transfer Protocol (HTTP) and various modes of data communication used to collect nearly half a million data points dispersed across remotes sites in the state of Oregon to date.

Increased demand for precision agriculture is reflected by a global rise in greenhouse food production. To maximize crop efficiency and yield, commercial greenhouses require live monitoring of growth conditions. Recent advances in open-source hardware allow for environmental sensing with the potential to rival lab-grade equipment at a fraction of the cost. This study introduces a high-resolution sensor package that costs less than $400. Consisting of microcontrollers and small open-source hardware, the sensor package can be deployed on the HyperRail, a modular conveyance system developed in Oregon State University’s OPEnS Lab. The system can then provide data from multiple sensing locations at the cost of a single package. Sensor data, including CO2, temperature, relative humidity, luminosity and dust/pollen, is saved to a microSD card as the HyperRail-mounted package travels throughout the greenhouse. A wireless GFSK nRF connection to a network hub allows the broadcast of a live stream of environmental conditions online. CO2 monitoring efforts are especially relevant to greenhouse management as artificially elevated levels can significantly increase plant growth. Results from calibration in the lab show that the K30 CO2 sensor ($85) can be calibrated to be accurate within less than 10 ppm of industry standard equipment costing up to $10,000. Our sensor package’s instructions, code, wiring, and 3D-printed enclosures are openly-published on GitHub. Addition of an RFID tag soil moisture sensing system is anticipated. Actuators may also be integrated in the future, allowing the system to automatically adjust greenhouse controls (i.e. CO2, water) in response to sensor readings. The affordability of this package can make precision agriculture more accessible in developing countries where conventional monitoring systems are not feasible. Efficient use of resources and the ability to adapt to local challenges with input from the open-source community has the potential to improve global crop yield.