NSF EAGER SitS: Autonomous Soil Nutrient Sensing System
New York Institute of Technology
Mission of the project
Revolutionizing Precision Agriculture with an Autonomous and Cost-effective Soil Nutrient Sensing System
Population growth, aggressive farming practices, and climate change have put significant stress on the food production system for sustainable growth. Agricultural runoff from over-fertilization and waste from large farms into natural water sources can cause algae outbreaks, and reduce the dissolved oxygen in water, resulting in ecosystem disturbance and a decline in fish populations. Precision agriculture with data-backed decision-making such as fine-grained spatiotemporal soil properties (moisture, temperature, pH, nutrients, organic matter, etc.) has the potential to improve the efficiency of farming practices by increasing crop growth and reducing agricultural runoff that contaminates surface and groundwater along with other negative environmental impacts. The current practice of soil property measurement relies heavily on taking samples for laboratory testing, which is costly and time-consuming for implementation over large areas. This project investigates a low-cost autonomous soil nutrient sensing system to support precision agriculture by integrating microelectromechanical sensors, ground penetrating radar, as well as autonomous unmanned aerial vehicle-enabled wireless sensor network.
Acknowledgment
This project was supported by the U.S. National Science Foundation under Grant No. 1841558.
Research Objectives
The research objectives of this project are to:
1) Identify specific polymers for nitrate and heavy metal ion detection
2) Design, fabricate, and validate the surface acoustic wave sensors
3) Design antennas and select proper ground penetrating radar for wireless measurement of the passive sensors
4) Integrate the data analysis and communication system
Explore Our Diverse Range of Engaging Research Topics
Wireless Sensing
SAW Sensor
Polymer Sensor
Soil Sensing with Drone and Ground Penetrating Radar
Autonomous Drone Path Planning
Wireless Sensing
Proposed wireless sensing system including the LimeSDR-mini, circulator, two antennas,
SAW device, and polymer (variable resistive load)
Soil Sensing Experiment Setup
The composition of different substances in soil has different effects on the plants. Therefore, a technique to detect soil composition is essential. Here, we investigate the application of software-defined radio (SDR) and surface acoustic wave (SAW) devices to be used for obtaining soil nutrient information. SDR uses software to realize different communication functions. By collecting the magnitude and phase of the response at discrete frequencies and applying the inverse Fourier transform, we analyze the time domain responses which, in turn, allows for monitoring the changes in nutrients in the soil. Comparing the normalized results obtained by SDR with those obtained from a commercial vector network analyzer (VNA), we demonstrated that the results are sufficiently close and the SDR-based experiments can be a good measure of soil nutrient sensing.
Surface Acoustic Wave Sensor
One-port SAW reflective delay line sensor with external load
The propagation speed of surface acoustic waves is almost 100,000 times slower than that of electromagnetic waves. In SAW devices, interdigitated transducers (IDT) are used to convert the input electrical signal into an acoustic signal. The acoustic signal propagates along the substrate material. Then, the IDT on the output converts the acoustic signal into an electrical signal to excite the polymer sensor. There are two reference reflectors set on the substrate material. When the acoustic signal encounters the reflector, a part of the signal will be reflected back. Thus, including the reflected signal from the polymer, there will be three reflection peaks in the time domain response. In the time domain there will be three peaks, peak 1 is the reflection from the reference open reflector. Peak 2 is the reflection from the reference reflector. Peak 3 is the reflection from the sensing reflector. Since the two reference reflectors are fixed, the time delay between peak 1 and peak 2 is also fixed. When the resistance of the polymer changes, the time delay between peak 3 and peak 1 or peak 2 will change. The resistance of the polymer can be deduced by observing the change of the time delay.
Polymer Sensor
Poly (3,4-ethylenedioxythiophene) (PEDOT)-Based Sensors for the Uptake and Detection of
Nitrate Ions
Nitrate pollution in groundwater, caused by various factors, including both natural and synthetic, contributes to the decline of human health and well-being. Current techniques used for nitrate detection include spectroscopic, electrochemical, chromatography, and capillary electrophoresis. It is highly desired to develop a simple cost-effective alternative to these complex methods for nitrate detection. Therefore, a real-time poly (3,4-ethylenedioxythiophene) (PEDOT) based sensor for nitrate ion detection via electrical property change was introduced in this study. Vapor phase polymerization (VPP) was the polymerization method used to create the polymer thin film. Variations in specific parameters during the vapor phase polymerization (VPP) process were tested and compared to develop new insights into PEDOT sensitivity towards nitrate ions. Through this study, it was determined that the optimal fabrication parameters that produce a sensor with the highest sensitivity toward nitrate ions are: a fabrication temperature of 42 °C, a pressure of -28.8 inHg, and a polymerization time of 50 min. The conductivity of the polymer film with these parameters is 683 S/cm. The response to 1000 ppm nitrate solution is 41.79%. The sensors can detect nitrate ranging from 1 ppm to 1000 ppm. The proposed sensor demonstrates excellent potential to detect the overabundance of nitrate ions in aqueous solutions in real-time.
Drone Path Planning
Proposed ORBIT, DETACH and STEER path planning algorithm generated route example with 50 waypoints
Intersections in multi-drone path planning increase the likelihood of drone collisions. We propose two offline collision-free multi-drone path-planning algorithms: DETACH and STEER. Large drone tasks can be divided into smaller ones that are carried out by multiple drones. Each drone follows a planned flight path that is optimized to efficiently perform its task. The path planning of the set of drones can then be optimized to complete the task in a short time, with minimum energy expenditure, or with maximum waypoint coverage. Here we focus on maximizing waypoint coverage. Different from existing schemes, our proposed offline path-planning algorithms detect and remove possible in-flight collisions. They are based on a constrained nearest-neighbor search that aims to cover a large number of waypoints in a flight-path. The proposed DETACH and STEER algorithms perform vector intersection checks for flight path analysis. We evaluate the waypoint coverage of the proposed schemes through a novel profit model and compare their performance in areas with different waypoint densities. Our results show that STEER covers 40% more waypoints and generates 20% more profit than DETACH in high-density waypoint scenarios.
Ground Penetrating Radar
pulseEKKO-PRO GPR Unit
Filtered GPR Data
In this set of experiments, the pulseEkko Pro Ground Penetrating Radar Unit from Sensors & Software was used to perform various types of scans of different objects under a sand bed. The radar unit includes a set of two antennas which are configured to send and receive signals at 250 MHz. The GPR measures peak amplitudes at varying depths to locate changing dielectric properties at the subsurface levels. The depth is known by the GPR marking the time it takes to receive a transmitted signal and converting it into depth, based on the known velocity of the signal traveling through the specific material. Eight objects were used in the scanning process with varying material, shapes, and sizes: stapler, metal weight plate, metal tray, wood block, hardcover book, and etc. The objects can be located by finding the depth at which the highest peak in the amplitude is located. In the experimental setup, the distance from the GPR to the objects was at approximately 0.3 m. Low pass filter and microwave shielding were applied to remove noise to improve sensitivity. Results show higher detection accuracy for larger and metal objects.
Conferences
BMES 2022
October 12 - 15, 2022
San Antonio, TX, Henry B. González Convention Center
Michael Kohler, M.S., currently pursuing a Ph.D. in Engineering, showcased his master's thesis work at the BMES 2022 conference held from October 12 to 15, 2022, at the Henry B. González Convention Center in San Antonio, TX. His research centered around the development and applications of PEDOT (poly(3,4-ethylenedioxythiophene)), a conductive polymer with promising electrical and mechanical properties. Michael's innovative work demonstrated techniques for synthesizing and characterizing PEDOT, as well as its integration into various engineering sensing systems. His presentation at BMES 2022 shed light on the immense potential of PEDOT in revolutionizing the fields of agricluture, electronics, and beyond.
Signals in the Soils Annual Workshop
May 17 - 19, 2022
At the Signals in the Soils Annual Workshop, held from May 17 to 19, 2022, our team proudly presented a captivating poster showcasing our groundbreaking system. With meticulous detail, the poster depicted the wireless sensor, saw sensor, and pedot soil sensor. These remarkable technologies collectively revolutionize soil monitoring, providing comprehensive insights into soil composition and health.
IEEE Wireless and Microwave Technology Conference (WAMICON) 2019
April 8-9, 2019,
Cocoa Beach, FL
MSc student, Kunyi Zhang, attended IEEE WAMICON 2019 conference and presented his research work in an oral presentation. His paper was also selected as a student paper finalist and he also presented a poster in front of judges. His work was on the use of microwave sensor arrays for sensing pollutants in water samples.
Publications
Michael C. Kohler, Fang Li, Ziqian Dong, Reza K. Amineh, "Real-Time Nitrate Ion Monitoring with Poly(3,4-ethylenedioxythiophene) (PEDOT) Materials," Sensors, 2023; 23(17):7627. https://doi.org/10.3390/s23177627
Yihan Xu, Reza K. Amineh, Ziqian Dong, Fang Li, and Michael Kohler, “Wireless Sensing with Software Defined Radio,” the 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, July 23-28, 2023, Portland, OR, USA.
Michael Kohler, Fang Li, Ziqian (Cecilia) Dong, Reza K. Amineh, “Poly (3,4-ethylenedioxythiophene) (PEDOT)-Based Sensors for the Uptake and Detection of Nitrate Ions,” Biomedical Engineering Society (BMES) Annual Meeting, San Antonio, TX, Oct. 12-15, 2022.
K. Shen, R. Shivgan, J. Medina, Z. Dong, and R. Rojas-Cessa, “Multi-Depot Drone Path Planning with Collision Avoidance,” in IEEE Internet of Things Journal, 2022, https://doi.org/10.1109/JIOT.2022.3151791
Jian Chu, Ioana Voiculescu, Ziqian Dong, and Fang Li, “Passive Impedance-loaded Surface Acoustic Wave (SAW) Sensor for Soil Condition Monitoring,” accepted in the Proceedings of the ASME 2020, International Mechanical Engineering Congress and Exposition, IMECE2020, November 16-19, 2020, Portland, OR, USA
Rutuja Shivgan and Ziqian Dong, “Energy-Efficient Drone Coverage Path Planning using Genetic Algorithm,” IEEE International Conference on High Performance Switching and Routing (HPSR 2020), May 11-14, 2020, Virtual Conference.
Zhang, Kunyi and Amineh, Reza K. and Dong, Ziqian and Nadler, David (2019). A Microwave Sensor Array for Water Quality Testing. 2019 IEEE 20th Wireless and Microwave Technology Conference (WAMICON). 1 to 4.
Zhang, Kunyi and Amineh, Reza K. and Dong, Ziqian and Nadler, David (2019). Microwave Sensing of Water Quality. 7. IEEE Access, 7. 69481 to 69493.
Team Members
Ziqian (Cecilia) Dong, Ph.D.
PI
Fang Li, Ph. D.
Co-PI
Reza K. Amineh, Ph. D.
Co-PI
Shenglong Zhang, Ph.D.
Co-PI
Michael C. Kohler, M.S.
Yihan Xu, M.S.
Mikhail Smirnov, M.S.
Past members
Rutuja Shivgan, M.S.
Kun Shen, M.S.
Kunyi Zhang, M.S.
Jay Patel, M.S.
Kayla Kirton, M.S.
Jian Chu, M.S.
