Progress 06/01/21 to 05/31/22
Outputs
Target Audience:Soil science research community, agricultural engineering research community, and the agricultural community. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project provided scientific and professional training to one graduate student in electrical engineering and sensordevelopment, one graduate student in sensor network, and another graduate student in computational mathematics. The project provided scientific and professional training to a postdoc in agronomy who works on agriculturalsystems process-based models. One electrical engineering Ph.D. studentrecentlygraduated from this project. His research was to develop sensors for the project. He now worksin a company that manufactures sensors. How have the results been disseminated to communities of interest?1. The team disseminated the research results through one peer-reviewed journal paper published in IEEE Sensors Journal (Chen, Y., Tang, Z., Zhu, Y., Castellano, M.J. and Dong, L., 2021. Miniature Multi-Ion Sensor Integrated With Artificial Neural Network.IEEE Sensors Journal,21(22), pp.25606-25615.) 2. The result on the nitrate sensors was disseminated through a Ph.D. thesis (Chen, Yuncong. "In-situ soil water potential sensor and nutrient sensor." Iowa State University, 2021). What do you plan to do during the next reporting period to accomplish the goals?We will use a control and analytics tool to integrate andfuse field sensor point data and remote sensor data topredict temporal and spatial variations inplant and soil nitrogen and water. We will focus ono using the generated data toprovide feedback to decision-making for irrigation and fertigation. We will continue to provide diverse STEM workforce training and mentorship activities,enhancing the graduate program in sustainable agriculture, recruiting underrepresented minority groups and women students,and transmitting knowledge to the public.
Impacts
What was accomplished under these goals?
To improve nitrogen sensor selectivity, weincorporated artificial neural network (ANN) algorithms with ourmulti-ion sensor to reduce cross-sensitivity between multiple sensing elements of the sensor for improving accuracy in measuring nitrate (NO3-), phosphate (H2PO4-), and potassium (K+) ions in agricultural soil solution, plant sap, and tile drainage water. An array of three ISE-based sensing elements were formed on one side of a printed circuit board (PCB). Each ISE element was coated with an ISM that ideally can target a specific NO3-, H2PO4-, or K+ ion (Fig. 1a and 1b). On the other side of the PCB was an Ag/AgCl-based RE. The sensor was shaped to a needle that helped to insert into the stalk of plants for in-situ measurement of target ions, while for ion sensing in soil solution and tile drainage water, the needle shape was not required. An ANN model was constructed to reduce the cross-sensitivity of the three ISEs through modeling the relationship between the input and output data of the neural network. The ANN was trained using the responses of the three ISEs to prepare mixed-ion solutions with known NO3-, H2PO4- and K+ concentrations. Key parameters of the ANN were optimized to improve performance in predicting NO3-, H2PO4- and K+ ion concentrations. Prediction performance of the ANN was evaluated by root mean squared error (RMSE) and coefficient of determination (R2). The three ISE-based sensing elements had different sensing characteristics and could probe differential interactions with ion species. The ANN analyzed these interactions and learned a model based on a part of the data to perform data classification and regression. The multi-ion sensor, in conjunction with the optimal ANN, was demonstrated to identify and quantify NO3-, H2PO4- and K+ ions in different samples obtained from agriculture cropland. We installed 18 soil nitrate sensors, 18 plant nitrate sensors, 18 leaf transpiration sensors, and 6 soil water potential sensors. These sensors were distributed between the two sites in Nebraska and Iowa. All sensors were powered by solar panels. Data wasinstalled on the SD card of eachsensor. The data logger of each sensorwasplaced inside a waterproof container on the ground.Allsensors were left in the fields for about one and half months after theirinstallation. However, due to the parts shortage and backorder, the installation started in early July, which was quite late.Data analysis is ongoing.We have noticed that7out of18 soil nitrate sensors, 8 out of 18plant nitrate sensors, 15 out of 18 leaf transpiration sensors, and 2 out of 6 soil water potential sensors could producewater and nitrate concentration data continuously from the sensor installation to retrieving.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Chen, Yuncong, Zheyuan Tang, Yunjiao Zhu, Michael J. Castellano, and Liang Dong. "Miniature Multi-Ion Sensor Integrated With Artificial Neural Network." IEEE Sensors Journal 21, no. 22 (2021): 25606-25615.
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2021
Citation:
Chen, Yuncong. "In-situ soil water potential sensor and nutrient sensor." Iowa State University. Ph.D. Disseratation (2021).
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Progress 06/01/20 to 05/31/21
Outputs
Target Audience:Soil science research community, agricultural engineering research community, andthe agricultural community. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?1. The project provided scientific and professional training to one graduate student in electrical engineering and sensor development, one graduate student in sensor network, and another graduate students in computational mathematics. 2. The project provided scientific and professional training to a postdoc in agronomy who works on agricultural systemsprocess-basedmodels. 3. One graduate student gave a presentation on the topic of plant VOC sensors atthe IEEENano/Micro Engineered and Molecular Systems (IEEE-NEMS),2020, virtual conference. The title of the presentation is "Wearable Sensors for On-Leaf Monitoring of Volatile Organic Compounds Emissions from Plants." 4. One graduate student gave a presentation on the topic of plant growth sensors atthe IEEENano/Micro Engineered and Molecular Systems (IEEE-NEMS),2020, virtual conference. The title of the presentation is "Capturing subtle changes during plant growth using wearable mechanical sensors fabricated through liquid-phase fusion." How have the results been disseminated to communities of interest?The team disseminated the research results through one peer-reviewed journal paper published in Advanced Materials Technologies (Adv. Mater. Technol. 2021, 2001246) and two presentations at the IEEE NEMS 2020 (virtual conference). What do you plan to do during the next reporting period to accomplish the goals? We plan to develop a control and analytics tool that can fuse field sensor point data (temporally-continuous, direct, detailed, individual-plant) and remote sensor data (spatially-continuous, indirect, entire field), predict temporal and spatial variations in plant and soil nitrogen and water, and provide feedback to decision-making, specifically targeted irrigation and fertigation. We will start to validate the proposed CPS for irrigation and fertilization optimization through empirical demonstrations using existing in-house and in-field testbeds, and to model, predict, and explain interactions and tradeoffs between NUE and WUE for production. At the same time, we will continue to provide diverse STEM workforce training and mentorship activities, enhancing the graduate program in sustainable agriculture, recruiting underrepresented minority groups and women students, and transmitting knowledge to the public.
Impacts
What was accomplished under these goals?
We optimized the nitrate sensor, leaf sensor, and soil water potential sensor to make them more rugged and robust for field deployment. The nitrate sensor was improved to achieve stable working and reference electrodes. The working electrode was formed with a thin layer of Ag deposited on a patterned Au electrode and covered with the ion-to-electron transducing layer and the nitrate-selective membrane. The reference electrode comprised a screen-printed silver/silver chloride electrode covered by a protonated polymer layer to prevent chloride leaching in long-term measurements. A waterproof epoxy covered the entire surface of the sensor and allows only the center area of the membrane to be exposed to the soil solution. The sensors provided long-term, continuous measurement of soil solution nitrate concentration at a specific point in space where they were deployed. The wearable transpiration sensor was improved to realize real-time on-leaf monitoring of relative humidity, temperature, and vapor-pressure deficit of plants in both controlled environments and under field conditions. This sensor became flexible and conformable to the leaf surface. By integrating a graphene-based RH sensing element and a gold-based thin-film thermistor on a polyimide sheet, the sensor allowed accurate and continuous determination of vapor-pressure deficit at the leaf surface, thereby providing information on plant transpiration. A greenhouse experiment validated the ability of the sensor to continuously and simultaneously monitor both the leaf RH and temperature of maize plants over more than two weeks. The sensor output also demonstrated the influences of light and irrigation on maize transpiration. By attaching multiple sensors onto different locations of a plant, it was possible to estimate the time required for water to be transported from the roots to each of the measured leaves along the stalk, as well as longitudinally from one position on a leaf toward the leaf tip. The sensors were also deployed in crop production fields where they demonstrated the ability to detect difference in transpiration between fertilized and unfertilized maize plants. The improved soil water potential sensor filled with an osmotic solution for continuous, in-situ monitoring of water potential with self-compensation of temperature. The sensor consisted of an active sensing element filled with an osmotic solution, and a reference element that was identical to the active element except for being sealed to prevent the embedded osmotic solution from transportation. The active element responded to both external soil water potential and environmental temperature changes, but the reference element only responded to temperature changes since the reservoir is sealed and the water transport was prevented. Therefore, the integration of the active and reference elements into a single water potential sensor could effectively compensate for temperature variation induced measurement errors that otherwise would be observed with the active element alone. When the environmental temperature changed, the volume of the osmotic solution in both active and reference elements change, leading to a displacement on the diaphragm. This displacement was then detected by the optical detector. Since the displacements on the active and reference elements were simultaneous, the temperature influence could be compensated by subtracting the output of the reference element from the output of the active element. Thus, there was no need for calibrating the response of the sensor to temperature. The improved soil water potential sensor extended the dynamic range of water potential to -1.1 MPa with almost no response to temperature variation. The sensor has been validated, demonstrating the ability to continuously monitor dynamic changes in water potential for about two weeks, conservatively. We used commercial wireless communication technology LoRaWAN for data collection from the sensor. All the field sensors will use rechargeable batteries powered by solar panels. The sensors, data collection units, and solar panels are currently being installed.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Yin, S., Ibrahim, H., Schnable, P.S., Castellano, M.J. and Dong, L., A Field-Deployable, Wearable Leaf Sensor for Continuous Monitoring of Vapor-Pressure Deficit. Advanced Materials Technologies, p.2001246, 2021. doi.org/10.1002/admt.202001246
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