1. Test and evaluation of a soil salinity sensor incorporated with
radio frequency identification technology
Erik Debye
Mentored by Dr. Robert Lieb
Introduction
Results
Conclusions
Fertilizer has long been used to increase crop yields. These fertilizers are mainly composed of three ingredients, Phosphorus, Nitrogen, and Potassium, which are all catalysts of plant growth and are found in the form of soluble salts. As a result of planting the same fields for several years, the addition of fertilizer to the soil has proven to be detrimental to crop yield (Lauchli et al., 1990). In addition, it has been shown that much of the fertilizer applied to fields is not absorbed by plants, resulting in environmental degradation (Tilman et al., 2002), and results in decreased profits for farmers (Babcock, 1992). Therefore, it would be beneficial to have the ability to track how much fertilizer is in the field prior to applying additional fertilizer. This knowledge has not only environmental benefits, but economic as well. Current systems to determine salinity, the amount of dissolved salts present in a material, are large and require the user to take measurements in the non-growing season. A common way to measure salinity is by measuring the electrical conductivity of the soil, measured in microsiemens (μS). This value is then calculated into what is known as the Total Dissolved Solids Factor (TDS) which represents the concentration of saline ions in the soil in parts per million (ppm).This is done with a simple conversion. Radio Frequency Identification (RFID) technology, which consists of a small data tag and a reader, is an emerging communications technology. This investigation developed the system with the open-source computing platform Arduino™ Uno, and provided proof-of-concept that demonstrated the device has the potential to greatly help the effort to reduce over-fertilization in the farming industry.
The results drawn from the data obtained in the experiment have three clear and distinct purposes. First, the results allow for the creation of calibration curves (Graph 1), which can then be utilized to estimate the concentrations of fields that used other concentrations of fertilizer, such as 67 pounds of fertilizer per acre. This aspect is crucial for use in reality, as it is hardly expected for each farmer to apply the same amount of fertilizer on their fields. In this investigation, the experiment was done utilizing fertilizer heavily based of nitrogen. In reality however, fertilizers are found with different ratios of the three nutrient elements, Nitrogen, Phosphorus, and Potassium, and so experiments would need to be conducted on each fertilizer in order to create an accurate calibration curve for a farmer to use with their fertilizer of choice. Second, it provides sufficient proof-of-principal for the device; the percent error found when comparing the measured Total Dissolved Salts Value to the true Total Dissolved Salts Value was found to be less than 1% in most of the trials. The study also created a sample plot (Graph 2) representing the relationship between TDS and bulk electrical conductivity that the device established, demonstrating proof-of-principle.
With industrial use, this device could prove significant in the battle against runoff from farming, which consists of a host of negative environmental effects. The device would exist to not prevent runoff, but rather prevent non-beneficial nutrients from ever being used. The use of RFID with the system contributes to a developing trend of RFID tags being incorporated into sensors, and prepares the device to be able to communicate with future systems.
Figure 1: Depicted is the circuit, using the Arduino™ Uno as the platform. The RFID tag is the black board found right.
Figure 2: Shown is the device being used to measure the bulk electrical conductivity of one of the experimental trays.
Materials and Methods
References
The components obtained were a 5TE Soil Salinity Sensor from Decagon Devices™ to measure bulk electrical conductivity and temperature, a Monza™ X-2K Dura RFID Development Kit to send results from the sensor to a RFID reader, and an Arduino™ Uno board upon which the device was built. The first priority of the study focused on wiring the devices together (Figure 1) and programing between the devices in the C programming language. The written program was required to instruct the sensor to collect data and send the values to the Arduino™ Uno, where it was converted into a TDS value, and finally that value is send to the RFID Tag. The study was then focused on providing proof-of-principle, in which three sets of soil trays were tested. Each set contained three trays (Figure 2): a control, a second tray simulating a concentration of 45 pounds of fertilizer per one acre of land, and a third tray simulating a concentration of 90 pounds of fertilizer per one acre of land.
Babcock, B. A. (1992). The effects of uncertainty on optimal nitrogen applications. Review of Agricultural Economics, 14(2),
Lauchli, A., Epstein, E., & Tanji, K. K. (1990). Agricultural salinity assessment and management. Agricultural salinity assessment and management.
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418(6898),
Graph 1: The graph displays the TDS Values over all sets, as well as the calibration curves created from the sets. The downward trend is indicative of salt leaching done by the addition of water to the soils.
Acknowledgments
I would like to thank my friends for all their support as well as my faculty advisor Mr. Sloan.
Graph 2: Depicted is a sample plot representing the relationship between TDS and electrical conductivity. As found to be strongly correlated, it provides proof-of-principle for the device.