Progress 03/01/23 to 02/29/24
Outputs
Target Audience:The project was presented in several seminars and workshops including Ag continuum work in eastern Iowa for ~20 participants, several open house events for local community and undergraduate students (~100 participants), and technical exchange meeting with Alliant Energy (~5 participants), the Chevron Renewable Energy Group (~10 participants), and local news (thousands of audiences).https://www.kcrg.com/2024/02/24/univ-iowa-student-highlights-importance-engineering-research/. The project was also presented in several professional meetings such as AGU and IEEE Transactions for Geoscience and Remote Sensing. The project also engaged with ~100 farmers in the west Nebraksa via the Univeristy of Nebraska - Lincoln'sextension office in Scott Bluff. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?In the University of Iowa, the following personnel has been involved in this project, including three postdoctoral researchers, one graduate student, and three undergraduates. For undergraduate and graduate students, they have received the opportunity through this project for being trained in the following areas: (a) data analysis (with Python programming) for comparing the weather forecast data with the in situ sensor data; (b) sensor design in which they work with Protostudio staff to test soil moisture sensors and evaluate the sensor functionality; (c) software design in which they are asked to write parts of the firmware for data transfer through the Internet of Things (IoT) protocol, and (d) critical thinking for transitioning what was learned in the classroom to what is needed in the reality. This critical thinking turned out to be very important and is often lacking if students are not exposed to the real- world applications. For example, we found that the the battery voltage and the WiFi and LoRaWan conncection are two key factors that often interrupt the in funciton of the I-Canopy sensor. By analyzing the the data (inclding voltage data) collected by sensors, we found that when the voltage is below certain value, the sensor will never be able to wake up, even though the voltage can be back up to higher values after the re-charge. After many trials and data analaysis, we now have refined the firmware to make the sensor more intellgent. When the voltage is is below certain threshold (3.4v), the sensor will turn itself into sleep and wait to wake up until the the voltage is high enough. Furthermore, the firmware to ask sensor to connec to WiFi is now limited up for 2 minutes per trial and the frequncy for such connection decreases as the number of failture accumulates. In this way, in the cases when the WiFI or LoRaWan are unstable or have connection, the sensor can be avoided to have frequent trail of connections with the WiFi, and consequently, loose the energy quickly . For postdoctoral researchers, this project has provided an unique opportunity for them to open their horizon and start to include the data collected from IoT sensors into their research work. In addition, by having them work with and to some degree, supervise graduate students and undergraduate students, they also gained valuable experience to mentor students and lead the group research, which can be beneficial for their future career development toward being a group leader. In University of Nebraska, two M.S. students and two undergraduate students are being trained on how to conduct field and lab-based experiments, analyze data, writing manuscripts and reports. Ms. Angie Gradiz also won the 2023 ASABE Annual International Meeting Presentation Excellence Award. Ms. Swathi Palle, who is funded by this project, was graduated in December 2023. Her thesis title is: "Responses of maize to different irrigation regimes in semi-arid western Nebraska". How have the results been disseminated to communities of interest?The results are disseminated to the communities through several pathways. The first is through the presentations, seminars, and workshopt for for targeted audience (see the targeted audience section). The second is via the conferences and publications (see the summary in publication and product section). The results have been presented to different groups such as local growers and commodity boards through workshops and field days. Results are also made available at professional conferences and journal publication. What do you plan to do during the next reporting period to accomplish the goals?While efforts will be taken to make steady progresses for all the objectives and hypothesis as originally planned in the proposal, the focus in the coming 12 months is to analyze the data we have in the past 5 years and do another field campain in western Nebraska. We also plan to publish the results from the analysis. We plan to have the intensive field experiments for the growing season in 2024. On the front of integrating model systems, we will continue our progress to integrate WRF with the crop model. The crop model is written in Delphi, while WRF is written in Fortran. We plan to compile the crop model in Linux (which will need some testing as the original code was running in windows). The crop model executable file will then read the WRF's output to predict the crop growth; the outputs of crop model and WRF output of precipitation and other weather variables will be used in the optimization model for irrigation and economic benefits. We plan to complete the integration of crop model with WRF in the first half of the incoming twelve months and then integrating all model components in the second half. On the front of community engagement, as the pandemic situation permits and folks all feel comfortable, we will use the UNL's extension facility in western Nebraska to outreach the farmers and rural citizens. We will also prepare on-line tutorial videos regarding the use of sensors, apps, and website.The engagement will be further strengthened by the new technology (e.g., smart phone apps) in which we can receive the users' feedbacks and respond in an interactive fashion
Impacts
What was accomplished under these goals?
The test and design of the canopy sensor system were fully completed and we were able to deploy 20 sensors in the rural communtiies in Nebraska for the intensive field campain in the growing season in Nebraska in 2023. The system is an integration of software and hardware via IoT protocol. In particular, the system is designed to be able to use both LoRaWan and WiFi. The LoRaWan is well suited for rural areas because of its low cost and its capability to transmit the data in long range. The firmware is now furhter updated to handle the unexpected disconnectoin of WiFi or LoRaWan. At the disconnection time, the firemare is modified to still collect the data and save them in the local disk/memory. when the connection is back, these data can then be transmitted back via IoT. In this way, no observation data is lost during the time where WiFi or LoRaWan connection breaks. The real-time irrigation scheduling tool based on weather forecast and soil moisture sensor information was developed and tested with farmers in eastern Nebraska. A journal paper was published. Another improvement of the firmware is for the energy mangement. In clear days, the rechargable battery can be frequently above 4.4V, which would lead to the reduciton of battery's life expectation. We have modified firmware such that when the battery is above 4.4V, the frequence to collect the measurements is increased to every 10 minutes (intead of every 25 minutes). Likewise, if the voltage is below 3.4V (like at the night in winter time), we will put the sensor into sleep and wait for the next day or next couple of days where the battery can be charged again. ?
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Liu, L., Qiao, X.*, Liang, W.Z., Oboamah, J., Wang, J., Rudnick, D.R., Yang, H.S., Shi, Y.Y. (2023). An edge-computing flow meter reading recognition algorithm optimized for agricultural IoT network. Smart Agricultural Technologies. https://doi.org/10.1016/j.atech.2023.100236.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Liang, W., Oboamah, J., Qiao, X.*, Ge, Y., Harveson, B., Rudnick, D. R., Wang, J., Yang, H., & Gradiz, A. (2023). CanopyCAM an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans. Computers and Electronics in Agriculture, 204, 107498. https://doi.org/10.1016/j.compag.2022.107498.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Jamal A., XM Cai, X. Qiao, L. Garcia, J. Wang, A. Amori, and H. Yang (2023). Real-time irrigation scheduling based on weather forecasts, field observations, and human-machine interactions, Wat. Resour. Res., https://doi.org/10.1029/2023WR035810.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Oboamah, J., Qiao, X., Liang, W.-Z. A Pivot-mounted IoT Sensing Node for Live Agricultural Data Collection. 2023 ASABE Annual International Meeting, Omaha, NE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Palle, S., Qiao, X., Liang, W.-Z., Gradiz, A., Oboamah, J., Van Anne, L., Rose, T. Responses of Crop Water Productivity and Growth Parameters for Maize under Different Irrigation Regimes in Semi-arid Western Nebraska. 2023 ASABE Annual International Meeting, Omaha, NE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Gradiz, A., Qiao, X., Liang, W.-Z., Palle, S., Oboamah, J., Rose, T., Van Anne, T., Irrigation Management for Dry Edible Beans in Western Nebraska. 2023 ASABE Annual International Meeting, Omaha, NE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Wang, S., J. Wang, L. Garcia, X. Qiao, T. Rose, and C. Reuben, Improvements of a smart-and-connected low-cost sensor system for measuring canopy properties in the central U.S., FRP.P19.4, IGARSS 2023, Pasadena, 16 21 July 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Wang, J., Impacts of irrigation, agriculture, and urbanization on regional climate and air quality, Seminar Series in Chemical & Biochemical Engineering, Missouri University Science & Technology, Rolla, MO, Sep., 2023.
|
Progress 03/01/22 to 02/28/23
Outputs
Target Audience:1. The project was presented and enaged with 20 farmers via a workshop for Nebraska Growers, 15 farmers via farm research venue, and 100 growers and stakeholders via field day in 2022. 2.Three presentations were given at professional conferences (ASABE and ASA/CSSA/SSSA) in 2022. 3. In March 2022, we organized a workshop with 12 invited farmers in Nebraska and tested our irrigation scheduling tool. Feedback from the participants was used to improve the tool. Attendants showed interests in using the tool in their irrigation practice 4. The project was prenseted in several seminars including departemtnal seminar inMichigan Technological Institute (30 participants) and inRochester Institute of Technology (40 participants), and via the workshop organized byNOAA Climate and Global Change Workshop,Rochester Institute of Technology in 2022. 5. The project was presented in the workshop on smart and connected community organized by NSF in Washington DC inOctober 11, 2022. 200 participants.? Changes/Problems:We may need to ask for another year of non-cost extension as the pandemic delayed our planned field campain for two years. What opportunities for training and professional development has the project provided?In the University of Iowa, the following personnel has been involved in this project, including three postdoctoral researchers,one graduate student, and three undergraduates. For undergraduate and graduate students, they have received theopportunity through this project for being trained in the following areas: (a) data analysis (with Python programming) forcomparing the weather forecast data with the in situ sensor data; (b) sensor design in which they work with Protostudio staffto test soil moisture sensors and evaluate the sensor functionality; (c) software design in which they are asked to write partsof the firmware for data transfer through the Internet of Things (IoT) protocol, and (d) critical thinking for transitioning what waslearned in the classroom to what is needed in the reality. This critical thinking turned out to be very important and is oftenlacking if students are not exposed to the real- world applications. For example, we found that the the battery voltage and the WiFi andLoRaWan conncection are two key factors that often interrupt the in funciton of the I-Canopy sensor. By analyzing the the data (inclding voltage data) collected by sensors, we found that when the voltage is below certain value, the sensor will never be able to wake up, even though the voltage can be back up to higher values after the re-charge. After many trials and data analaysis, we now have refined the firmware to make the sensor more intellgent. When the voltage is is below certain threshold(3.4v), the sensor will turn itself into sleep and wait to wake up until the the voltage is high enough. Furthermore,the firmware to ask sensor to connec tto WiFi is now limited up for 2 minutes per trial and the frequncy for such connection decreases as the number of failture accumulates. In this way, in the cases when the WiFI or LoRaWan are unstable or have connection,the sensor can be avoided to have frequent trail ofconnections with the WiFi, and consequently,loosethe energy quickly .For postdoctoral researchers, this project has provided an unique opportunity for them to opentheir horizon and start to include the data collected from IoT sensors into their research work. In addition, by having them work with and to somedegree, supervise graduate students and undergraduate students, they also gained valuable experience to mentor studentsand lead the group research, which can be beneficial for their future career development toward being a group leader. In University of Nebraska, two M.S. students and two undergraduate students are being trained on how to conduct field and lab-based experiments, analyze data, writing manuscripts and reports. How have the results been disseminated to communities of interest?The results are disseminated to the communities through several pathways. The first is through the presentations, seminars, and workshopt for fortargeted audience (see the targeted audience section).The second is via the conferences and publications (see the summary in publication andproduct section).The results have been presented to different groups such as local growers and commodity boards through workshops and field days. Results are also made available at professional conferences and journal publication. What do you plan to do during the next reporting period to accomplish the goals?While efforts will be taken to make steady progresses for all the objectives and hypothesis as originally planned in theproposal, the focus in the coming 12 months is to analyze the data we have in the past 4 years and do another field campain in western Nebraska. We also plan topublish the results from the analysis.We plan to have the intensive field experiments for the growing season in 2023. On the front of integrating model systems, wewill continue our progress to integrate WRF with the crop model. The crop model is written in Delphi, while WRF is written inFortran. We plan to compile the crop model in Linux (which will need some testing as the original code was running inwindows). The crop model executable file will then read the WRF's output to predict the crop growth; the outputs of cropmodel and WRF output of precipitation and other weather variables will be used in the optimization model for irrigation andeconomic benefits. We plan to complete the integration of crop model with WRF in the first half of the incoming twelve monthsand then integrating all model components in the second half. On the front of community engagement, as the pandemic situation permits and folks all feel comfortable, we will use theUNL's extension facility in western Nebraska to outreach the farmers and rural citizens. We will also prepare on-line tutorialvideos regarding the use of sensors, apps, and website.The engagement will be further strengthened by the new technology (e.g., smart phone apps) in which we canreceive the users' feedbacks and respond in an interactive fashion.
Impacts
What was accomplished under these goals?
The test and design of thecanopy sensor system were fully completed and we were able to deploy 15 sensors in the rural communtiies in Nebraska for the intensive field campain in the growing season in Nebraska in 2022. The systemis an integration of software and hardware via IoT protocol. In particular, the system is designed to be able to use bothLoRaWan and WiFi. The LoRaWan is well suited for rural areas because of its low cost and its capability to transmit the datain long range. The paper that described the sensor design was also published in 2022. Another signficant accomplishment was the engagement with rural communties via workshop. The irrigation scheduling software was presented to ~20 farmers and we seeked their inputs on the software, and learned from them about their decision process for irrigation scheduling.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Jamal, A. and XM Cai (2022). Real-Time Irrigation Scheduling Modeling, Water Congress, Environmental and Water Resources Institute (EWRI), Am. Soc. Of Civil Engineers (ASCE), June 2022, Atlanta, GA.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2023
Citation:
Liang, W., J. Oboamah, X. Qiao, YY. Ge, B. Harveson, D.R. Rudnick, J. Wang, H. Yang, A. Gradiz, CanopyCAM an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans, Computers and Electronics in Agriculture, 204, 107498, 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Ru, Z. J. Wang, S. Kuhl, L. C. Garcia, X. Qiao and D. Reed, A smart-and-connected low-cost sensor system for measuring air and soil properties in the Central U.S.: first results, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 5720-5723, doi: 10.1109/IGARSS46834.2022.9884823, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Wang, J., Impacts of irrigation, agriculture, and urbanization on regional climate and air quality, Seminar Series in Earth, Planetary, and Space Sciences, Michigan Technological Institute, Houghton, MI. October 2022.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Wang, J., A smart-and-connected irrigation and weather intelligence system for rural communities in Nebraska, NOAA Climate and Global Change Workshop, Steamboad, CO, July 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Qiao, X., Internet-of-Things (IoT), edge-computing, and deep learning in agriculture. Agriculture Faculty Academy, Irrigation Association, June 2022. Online.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Qiao, X., Irrigation Management and Technologies in Western Nebraska. UniCEN Webinar. June 8th, 2022. Online.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Qiao, X. Irrigation Engineering and Computer Science. Western Nebraska Community College STEM Series. April 6th, 2022. Scottsbluff, NE.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Jamal, A., XM Cai, X. Qiao, L. Garcia, J. Wang, A. Amori, and H. Yang (2022). Real-Time Irrigation Scheduling based on Weather Forecasts, Field Observations, and Human-Machine Interactions, Water Resources Research, under review.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Qiao, X., Peer-Learning Agricultural Network (PLAN). ASA, CSSA & SSA International Annual Meetings. November 2022. Baltimore, MD.
|
Progress 03/01/21 to 02/28/22
Outputs
Target Audience:Our targeted autidenes are primarily farmers and researchers. However, due to the pandemic, outreach in person has been difficult. More or less, I beleive we did out best in the past year. The project was presented to the Panhandle Ag and Research Technology Tour, Scottsbluff, NE. Aug. 2021. Number of participates: 80. In person. The project was presented to the Soil Water Sensor-based Irrigation Management Workshop, March 22nd, 2021. Number of participants: 15 Attendees. Online platform. The project was presented to the American Geophysical Union, session on precision agriculture, 13 December 2021. Number of participants: 50. Hybrid (in person and online platform). The project was presented to the Department of Geography and Sustainability at the University of Iowa, Monday, October 4, 2021. Number of participates: 60. Online platform. Changes/Problems:Because of the delay associaed with the pandmeic and the departure of one postdoc that interrupte the process, we plan to request one year extension to complete the project. What opportunities for training and professional development has the project provided?In the University of Iowa, the following personnel has been involved in this project, including three postdoctoral researchers, one graduate student, and three undergraduates. For undergraduate and graduate students, they have received the opportunity through this project to being trained in the following areas: (a) data analysis (with Python programming) for comparing the weather forecast data with the in situ sensor data; (b) sensor design in which they work with Protostudio staff to test soil moisture sensors and evaluate the sensor functionality; (c) software design in which they are asked to write parts of the firmware for data transfer through the Internet of Things (IoT) protocol, and (d) critical thinking for transitioning what was learned in the classroom to what is needed in the reality. This critical thinking turned out to be very important and is often lacking if students are not exposed to the real- world applications. For example, we found that the Stevenson screen case works well in the campus of University of Iowa, but when our sensors are moved the farm fields in the state of Nebraska, they face the problems because the irrigation water can reach the case from all directions, not just from the sky (like rain) but also from the side and down below. It turned out that it is extremely difficult to measure air temperature, pressure, and relative humidity in the irrigation field. For postdoctoral researchers, this project has provided a unique opportunity for them to open their horizon and start to include the data from IoT into their research. In addition, by having them work with and to some degree, supervise graduate students and undergraduate students, they also gained valuable experience to mentor students and lead the group research, which can be beneficial for their future career development toward being a group leader. In University of Nebraska, two M.S. graduate students are funded through this project. They worked together with one MS student from the University of Iowa in summer 2021 to install the smart-and-connected canopy sensors. They also worked together to use machine-learning tools to calibrate the soil moisture sensors. In the University of Illinoi Urbana-Champaign, one postdoctoral student is trained to take the observation data and model puts to develop an irrigation optimization model that takes into account weather forecast, crop growth, and the prices of Agricultural Commodities. We alsoplan to explain the model function and test the model with farmers via a workshop in early March of 2022. How have the results been disseminated to communities of interest? The results are disseminated to the communities through several pathways. The first is through the media including local newspapersand social media (as briefly summarized in the section for targeted audience and accessible at https://arroma.uiowa.edu/news.php). The second is via the conferences and publications (see the summary in publication and product section). Finally, we have engaged communities through extension services, emails, career fair and research fair organized by the universities and local communities. Becaus of the pandemic, disseminating the results to the communities of interest has been changlleing. But, in the fall of 2021, we did showcase our work via the research open-house activiteis to many citizens from rurual communites in Iowa. What do you plan to do during the next reporting period to accomplish the goals? While efforts will be taken to make steady progresses for all the objectives and hypothesis as originally planned in the proposal, the focus in the coming 12 months is to integrate all parts in the our study area in western Nebraska. These parts inlcude the canopy sensor system (we alread built and rigiroulsy tested),different models from irrigaiton optimization model to weather forecast model, and the community engagement. We plan to have the intensive fieldexperiments for the growing season in 2022. Since the integrated sensors (via LoRaWan and WiFi) have been already vigorously tested, we plan to deploy at least 50 of these canopy sensor systemsto the western Nebraska. In fact, we already ordered all the parts for 80 of these sensor systems and we are in the process to manufacuture them. On the front of integrating model systems, we plan to integrate the crop model with WRF's output. In the past 12 months, we are able to run the WRF at 3 km for the region of our study. However one postdoc left the group unexpeclty, and so, we had some delay in our plan to add the crop model into WRF. Now, we just hired a postdoctoral researcher in last winter, and we will acclerate our progress to integrate WRF with the crop model.The crop model is written in Delphi, while WRF is written in Fortran. We plan to compile the crop model in Linux (which will need some testing as the original code was running in windows). The crop model executable file will then read the WRF's output to predict the crop growth; the outputs of crop model and WRF output of precipitation and other weather variables will be used in the optimization model for irrigation and economic benefits. We plan to complete the integration of crop model with WRF in the first half of the incoming twelve months and then integrating all model components in the second half. On the front of community engagement, as the pandemic situation permits and folks all feel comfortable,wewill use the UNL's extension facility in western Nebraska to outreach the farmers and rural citizens. We will also prepare on-line tutorial videos regarding the use of sensors, apps, and website. A social media channel through Facebook for this project is also in planned. The engagement will be further strengthened by the new technology (e.g., smart phone apps) in which we can receive the users' feedbacks and respond in an interactive fashion.?
Impacts
What was accomplished under these goals?
The biggest accomplishments are that the canopy sensor system is fully tested and is ready for production. The system is an integration of software and hardware via IoT protocol. In particular, the system is designed to be able to use both LoRaWan and WiFi. The LoRaWan is well suited for rural areas because of its low cost and its capability to transmit the data in long range. The canopy sensor system manufactured by University of Iowa is tested at several commercial production fields as well as at research farms. At the research farm, the canopy sensors were compared against HydraProbe (Stevens Water Monitoring Systems Inc., Portland, Oregon) for evaluation of performance and accuracy. Commercial soil water sensors (Watermark 200SS, Irrometer Inc., CA) were installed at 23 commercial production fields, including 10 dry bean fields, 8 corn fields, 3 sugar beet fields, and 2 alfalfa fields. Total area of participating fields were 2542 acres, a significant increase from 1044 acres in 2020. Soil water sensor data was lively transmitted and displayed at websitehttps://phrec-irrigation.com/. Irrigation recommendations were provided to growers during the growing season, which resulted a total of 680 acre*in water savings. Sensor, yield, and field management data were provided to U of Iowa and UIUC for modeling purposes. A real-time irrigation scheduling model was established and tested with two crop fields for the irrigation season of 2019. The model recommends irrigation date and amount during the irrigation season based on soil moisture provided by sensors, weather forecasts, and farmers actual irrigation decisions up to a study time point.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
1. Wang, J., L. Castro Garcia, Z. Ru, N. Janechek, X. Qiao, X. Cai, H. Yang, D. Rudnick, A. Kasabian, and D. Reed, Development of a smart-and-connected irrigation system for rural communities in Nebraska, IN11A-05, AGU 2021 Fall Meeting, New Orleans, 12-17 December 2021.
2. Jamal A. and XM Cai (2021). Stochastic irrigation scheduling based simulation-optimization: a human-machine interactive method enhanced by data assimilation. Am. Geophysical Union (AGU), fall meeting 2021, New Orleans, LA.
3. Qiao, X., Liang, W.-Z., Oboamah, J., Stone, G. A sensor and IoT-based research and extension learning network in rural western Nebraska. ASA, CSSA, SSSA International Annual Meeting. Salt Lake City, UT. November 7-10, 2021.
4. 2. Oboamah, J., Liu, L., Qiao, X., Liang, W.-Z. An AI-based flow meter readings recognition framework in an agriculture IoT setting. ASA, CSSA, SSSA International Annual Meeting. Salt Lake City, UT. November 7-10, 2021.
5. 3. Liang, W.-Z., Oboamah, J., Qiao, X., & Liu., L. Development of a LoRaWAN research and extension platform in rural western Nebraska. ASABE Annual International Meeting (virtual), July 12-16, 2021.
6. 4. Liu, L., Oboamah, J., Liang, W.-Z., Qiao, X., & Shi, Y. An AI-based Flow Meter Readings Recognition Framework in An Agriculture IoT Setting. ASABE Annual International Meeting (virtual), July 12-16, 2021.
7. 5. Qiao, X., Liang, W.-Z., Oboamah, J., Stone, G. PLAN: An IoT-based Learning Agricultural Network in the Nebraska Panhandle. Virtual Brainstorming workshop on AI and Sensing for Monitoring and Management of Pests and Diseases in Rice , Purdue University and Indian Council of Agricultural Research (ICAR), Sep. 10-11, 2021.
8. 6. Qiao, X., Liang, W.-Z., Oboamah, J., Stone, G. PLAN: An IoT-based Learning Agricultural Network in the Nebraska Panhandle. Nebraska Water Conference, Aug. 2021. Scottsbluff, NE.
9. 7. Qiao, X., Liang, W.-Z., Oboamah, J. Internet of Things and its Ag Applications. Oklahoma Master Irrigator Program, Feb. 2021. Online.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Sha, T., X. Ma, H. Zhang, N. Janechek, Y. Wang, Y. Wang, L. Castro, G. Jenerette, J. Wang, Impacts of soil NOx emission on O3 air quality in rural California, Environmental Science & Technology, 55, 7113?7122, DOI: https://doi.org/10.1021/acs.est.0c06834, 2021.
|
Progress 03/01/20 to 02/28/21
Outputs
Target Audience: Before the pandemic, the NE team has organized a workshop titled "2020 Panhandle Ag Internet of Things Workshop and Round Table" on March 5th in Scottsbluff, NE. There were around 30 attendees with diverse backgrounds such as farmers, local environment agency officials, high school STEM teacher, city water facility management official, and university employees. Detailed survey was sent out and a professional report was generated to evaluate effectiveness of the workshop. The project was presented to ~100 undergraduates in the classes that PI taught in the University of Iowa. These classes include: (a) statistical analysis class, (b) senior design class, and (c) atmospheric modeling class. Admittedly, the project'soutreach to the audiences is rather limited during the pandemic. Changes/Problems:There is no major changes of objectives, except that the progress is delayed due to the pandemic. What opportunities for training and professional development has the project provided?The project has provided several opportunities for training and professional development for the communities of interest. First, the irrigation tool can provide a suggested irrigation schedules for historical growing seasons so that the farmers can compare this improved irrigation schedule and estimated profit against the corresponding obtained profit based on the farmers irrigation. Second, the tool can explain and quantify the shortages of the farmers irrigation schedules in terms, for example, of water stress or water excess. Third, the tool can provide information on the expected importance of the irrigation during several periods throughout the growing season to maximize the profits based on sensitivity analysis. The development of integrated smart and connected sensors provides students important opportunities to have hands-on projects that connect with real-world application. For example, student learned that an engineering system that works perfectly at the lab setting may not work well at the ambient settings, 24/7. This is because in the ambient atmosphere, weather can be harsh and in sometimes, has extreme cold or hot conditions. It took students several trials to redesign circuit and energy management system so that the sensor can work at extreme cold conditions in Iowa. In the process of smart sensor development, students/postdocs are exposed to learn many new techniques (such as soldering, sensor calibration, firmware design, etc)and an integrated approach to tackle problems. For undergraduate and graduate students, they have received the opportunity through this project to being trained in the following areas: (a) data analysis (with Python programming) for comparing the weather forecast data with the in situ sensor data; (b) sensor design in which they work with Protostudio staff to test soil moisture sensors and evaluate the sensor functionality; (c) software design in which they are asked to write parts of the firmware for data transfer through the Internet of Things (IoT) protocol, and (d) critical thinking for transitioning what was learned in the classroom to what is needed in the reality. Collectively, postdocs and undergraduate students are trained to work together on big project and to have holistic thinking of the project. How have the results been disseminated to communities of interest? The results will be disseminated to the communities of interest through workshops and meetings in which the analysis performance of the tool will be presented in front of the community, and where the community will have the opportunity to try several applications of the tool on the computers. In addition, field experiments will be carried out. The results are also disseminated to the communities via the conferences and publications (see the summary in publication and product section). We have engaged communities through extension services, emails, career fair and research fair organized by the universities and local communities.here are in total more than 20 farmers signed up to try our new sensors in western Nebraska.? What do you plan to do during the next reporting period to accomplish the goals? After finalizing the tool of irrigation scheduling, sub-tools of sensitivity analysis and evaluations will be developed. Next, hypothetical case studies will be examined in order to show the reliability of the tool. After that, the tool will be tested on historical growing seasons of the fields that belong to the community. Then workshops will be set in order to present these results in front of the community. A lab experiment of the irrigation scheduling tool with selected farmers is planned for the summer of 2021; a real-world application of the tool for selected testbeds is planned for 2022. On the front of integrating model systems, we plan to integrate the crop model with WRF's output. The crop model is written in Delphi, while WRF is written in Fortran. We plan to compile the crop model in Linux (which will need some testing as the original code was running in windows). The crop model executable file will then read the WRF's output to predict the crop growth; the outputs of crop model and WRF output of precipitation and other weather variables will be used in the optimization model for irrigation and economic benefits. We plan to complete the integration of crop model with WRF in the first half of the incoming twelve months and then integrating all model components in the second half. On the front of community engagement, we have made plans to have local workshops and townhall meetings with rural citizens in the western Nebraska. We will use the UNL's extension facility in western Nebraska to outreach the farmers and rural citizens. We will also prepare on-line tutorial videos regarding the use of sensors, apps, and website. A social media channel through Facebook for this project is also in planned. The engagement will be further strengthened by the new technology (e.g., smart phone apps) in which we can receive the users' feedbacks and respond in an interactive fashion.
Impacts
What was accomplished under these goals?
An integrated smart and connected sensor systemto measure relative humidity, pressure, and temperature at 2m and to measure soil temperature at one depth (10 cm), and soil moisture at 2 depth (5 cm and 20 cm) is fully developed and have been tested in the field. The systemis enabled by the Internet of Things (IoT) protocol, empowered by solar-based rechargeable batteries, and developed for citizen science applications. The sensor, the so-called canopy-air sensor, is designed for real-time monitoring of near-surface air properties (temperature, relative humidity, pressure) and soil properties (temperature and moisture) for a wide range of weather and canopy conditions. The sensor is well suited for rural areas where the real-time data of air and soil is lacking in part due to the lack of broadband internet connection, and in part due to the limited (if any) ground-based weather stations in the current federal and state observation network.The canopy-air sensor has been tested in rural communities in western Nebraska to provide information for farmer's decision-making of irrigation and agricultural water use in the crop growing season. The sensor is capable to transmit data through both WiFi and low-power long-range (LoRa) wide-area network (WAN) in real-time to Amazon cloud and the local data server. Presented here are the first results of the sensor design and sensor data evaluation in various out-door environments, which illustrates the high-level readiness of the sensor for large-scale deployment for either routine or scientific applications for rural areas. The data from the pilot experiment can be viewed athttp://esmc.uiowa.edu:3838/uiowa_sc/ andhttps://esmc.uiowa.edu/query_sensor.php, esp32v13, and in here, https://esmc.uiowa.edu/query_sensor_vcc.php,typeesp32v17. We have installed two more LoRa gateways and there are four gateways in total located at Scotts Bluff County (3) and Morrill County (1). All four gateways are operating. Locations of the four gateways can be seen at: https://phrec-irrigation.com/gateways.Within perimeter of the 4 gateways, we have installed soil water sensing stations at 6 commercial fields that belonged to 4 growers. Among which 5 were planted with dry edible beans, 1 was planted with corn; 5 were sprinkler irrigated and 1 was irrigated with subsurface drip irrigation system. At each sensing station, it is equipped with 1 data logger and 3 Watermark 200SS soil water sensors that were installed at 8, 16, and 24 inches, respectively. Data from these sensors were transmitted in near real-time and growers could access data readings using an in-house developed website: https://phrec-irrigation.com/platform/farms. Website has been continuously improved and modified upon communication and request from farmers. Data transmission from sensing stations to LoRa gateway were promising, as one sensing station was as far as 5 miles away from gateway, yet provided reliable and constant data transmissions. Routinely, we discussed with growers on how to interpret soil water content readings and also discussed irrigation management. The irrigation scheduling tool is under development. It consists of three sub-tools: calibration (developed), which takes into consideration historical data to initialize the tool from a field-suited conditions, assimilation(developed), which helps improving the tool to match the current field conditions based on data that is collected during the growing season, and optimization (under development) tools that aims to calculate the daily irrigation amounts based on weather forecasts and field conditions.
Publications
- Type:
Journal Articles
Status:
Under Review
Year Published:
2021
Citation:
Li, X., J. Zhang, Z. Huo, and XM Cai (2021), Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation, J. of Hydro., In Review.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
o Data driven agricultural research and extension , Guest lecture for STEM Connect Seminar Series at Western Nebraska Community College. Sep. 9th, 2020, Scottsbluff, NE
o Irrigation research update , Panhandle Agriculture Research and Technology Tour, Aug. 20th, 2020, Scottsbluff, NE
o Internet of Things and its applications for crop production in western NE , Nebraska Bean Day, Feb. 11th 2020, Gering, NE
o An introduction to Internet of Things and its Ag applications , Guest lecture for MSYM 892 Technologies and Techniques for Digital Agriculture. Feb. 4th, 2020
- Type:
Websites
Status:
Published
Year Published:
2020
Citation:
Wang, J., L. Castro, a website for weather forecast and citizen science project in the midwest, https://esmc.uiowa.edu
- Type:
Journal Articles
Status:
Other
Year Published:
2021
Citation:
An Edge-computing Flow Meter Reading Recognition Algorithm Optimized for Agricultural IoT Network . Planned to be submitted to Journal: Computers and Electronics in Agriculture
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Progress 03/01/19 to 02/29/20
Outputs
Target Audience:- The project was presented to North Platte Natural Resource District board meeting in March 2019 in Scottsbluff. Number of participants: 35. - The project was presented during Panhandle Research and Extension Center annual field day on 8/22/2019 in Scottsbluff. Number of audience 80. - The project was presented to Scottsbluff local water leadership group on 1/3/2020. Number of participants: 10. - The project was presented during an UNL flagship extension program "crop protection clinic" on 1/7/2020 that reached 80 people in Scotts Bluff County, Nebraska. - The project was presented to commodity boards in NE such as Nebraska Dry Bean Commission (NDBC), Western Sugar Cooperative (WSC) during their annual meetings. There were about 25 people in the NDBC meeting and 100 people in the WSC meeting. NDBC meeting was at Ogalla NE. WSC meeting was at Loveland, CO. - The project was presented to UNL BSE graduate students for MSYM 892 Digital Ag class over zoom on 2/4/2020. Participants number: 5. - The project was presented to commodity boards in NE such as Nebraska Dry Bean Commission (NDBC), Western Sugar Cooperative (WSC) during their annual meetings. There were about 25 people in the NDBC meeting and 100 people in the WSC meeting. NDBC meeting was at Ogalla NE. WSC meeting was at Loveland, CO. - The project was presented to UNL BSE graduate students for MSYM 892 Digital Ag class over zoom on 2/4/2020. 5 people attended this event. - The project is shared to the farmers in Iowa through three TV interviews, three news report, and multiple engagement with local communities. while it is difficult to estimate exactly how many people are informed through these medias, we were indeed asked by the local TV stations to give an update for our project in Nov since their report of our project in the spring was well received by the local community. I indeed received multiple inquiries about this project from local farmers. I would estimate that number of people our TV reports outreached to is on the orders of thousand. - The project also had a exhibitor in the campus-wide career fair in the University of Iowa. ~400 people dropped by and tried our sensors and watched our apps. - The project was also talked through WorldCanvass and Podcat through PRX. https://international.uiowa.edu/news/worldcanvass-recap-research-iowa-investigating-space-cancer-treatments-and-iowas-bioscience - The TV and news reports are can be found at here: https://arroma.uiowa.edu/news.php Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The following personnel has been involved in this project, including three postdoctoral researchers, one graduate student, and three undergraduates. For undergraduate and graduate students, they have received the opportunity through this project to being trained in the following areas: (a) data analysis (with Python programming) for comparing the weather forecast data with the in situ sensor data; (b) sensor design in which they work with Protostudio staff to test soil moisture sensors and evaluate the sensor functionality; (c) software design in which they are asked to write parts of the firmware for data transfer through the Internet of Things (IoT) protocol, and (d) critical thinking for transitioning what was learned in the classroom to what is needed in the reality. This critical thinking turned out to be very important and is often lacking if students are not exposed to the real-world applications. For example, to address how to design the firmware to be both energy efficient and user friendly with the consideration that wireless communication sometimes can be unstable, the students learned that there is no one-size-fits-all approach and it becomes an optimization problem to consider different tradeoffs and priorities. While graduate student focus more on the research and lab work, undergraduate students have spent more time to engage with the rural communities. For example, they made several trips to help schools and farms to install sensors. For postdoctoral researchers, this project has provided a unique opportunity for them to open their horizon and start to include the data from IoT into their research. In addition, by having them work with and to some degree, supervise graduate students and undergraduate students, they also gained valuable experience to mentor students and lead the group research, which can be beneficial for their future career development toward being a group leader. Finally, the last but not least, because this project also enables all students and postdoctoral researchers to work together to conduct interdisciplinary work, and the research team consists of scientists in different fields from atmospheric science to agronomy to civil engineering, electronic engineering, and biosystem engineering, this project also enables all students and postdoctoral researchers to work together to conduct interdisciplinary research. Such training opportunity can only be available in the interdisciplinary project like this one. How have the results been disseminated to communities of interest?The results are disseminated to the communities through several pathways. The first is through the media including local TV interviews, local newspapers, and social media (as briefly summarized in the section for targeted audience and accessible on https://arroma.uiowa.edu/news.php). The second is via the conferences and publications (see the summary in publication and product section). Finally, we have engaged communities through extension services, emails, career fair and research fair organized by the universities and local communities. A one-day workshop with rural citizens in the western Nebraska is now also scheduled on early March. What do you plan to do during the next reporting period to accomplish the goals?While efforts will be taken to make steady progresses for all the objectives and hypothesis as originally planned in the proposal, the focus in the coming 12 months will be the deployment of integrated sensors, the integration of different models, and the community engagement. The integrated sensor system can measure soil moisture and temperature at two levels and the relative humidity, pressure and temperature at the altitude of 2 meters above the ground. This integrated system is now in the final stage of testing and we plan to deploy the prototype version of these sensors in the field in April. The integrated sensor will use the LoRaWAN and so, the testing will be conducted in the farm field, which will prepare for the intensive experiments in the third year. Currently, 50 of these integrated sensors are planned for the deployment. On the front of integrating model systems, we plan to integrate the crop model with WRF's output. The crop model is written in Delphi, while WRF is written in Fortran. We plan to compile the crop model in Linux (which will need some testing as the original code was running in windows). The crop model executable file will then read the WRF's output to predict the crop growth; the outputs of crop model and WRF output of precipitation and other weather variables will be used in the optimization model for irrigation and economic benefits. We plan to complete the integration of crop model with WRF in the first half of the incoming twelve months and then integrating all model components in the second half. On the front of community engagement, we have made plans to have local workshops and townhall meetings with rural citizens in the western Nebraska. We will use the UNL's extension facility in western Nebraska to outreach the farmers and rural citizens. We will also prepare on-line tutorial videos regarding the use of sensors, apps, and website. A social media channel through Facebook for this project is also in planned. The engagement will be further strengthened by the new technology (e.g., smart phone apps) in which we can receive the users' feedbacks and respond in an interactive fashion.
Impacts
What was accomplished under these goals?
Achievements for Obj1. Sensor efficiency. The smart-and-connected sensor design is refined with newer hardware and firmware. We are able to deploy ~ 50 sensors to the rural citizens through a wide range of engagement activities including school visit, career fair, service extension conferences, and classroom teaching activities. The sensor's case is redesigned to prevent the inferences by the insects (such as bees) and moisture. The firmware is refined to accommodate the possibility of WiFi wireless malfunction at the citizen's house (e.g., intermittent problems that lead to the stop of WiFi for a couple of hours) as well as to provide a more friendly interface for users and a more turnkey experience for changing the battery of the sensor. With this new update, the sensors for measuring atmospheric parameters have high energy efficiency and the sensor's network is able to collect more data in a continuous mode. Sensor integration. We have also designed the scheme to integrate the soil moisture and soil temperature together with the weather sensor. Through testing of various soil moisture sensors that have a wide range of costs, we decide to use the soil moisture probe by Vegetronix. We are planning to prototype the sensor integration in April of 2002 and this integrated senor system that can measure the 2-meter relative humidity, pressure, and temperature together with the soil moisture and soil temperature at 10 cm and 20 cm levels under the ground surface. Of particular note is that these sensors will have solar panels and LoRaWan capabilities, which can facilitate the use of these sensors in the farm field. Testing in farm field. Two LoRaWAN gateways were deployed in our study region in Scottsbluff, NE. Several commercial soil moisture sensors were also deployed to test the range of LoRaWAN signal and overall connectivity for transmitting data from the farm field through LoRaWAN to the cloud storage using the IoT data transfer protocol (http://phrec-irrigation.com). Preliminary test is very encouraging, which effective pave the way for deploying our integrated sensor systems into the farm field in summer of 2020. Smart-and-connected systems. A three-day weather forecast system as well as the apps to deliver such weather were developed and completed. The web http://esmc.uiowa.edu allows users to look at the weather forecast at any given point of their interest in our study region (Nebraska and Iowa) by simply clicking on the map shown on this website. The web also hosts the stream of citizen science data collected through our sensor network in real time. The locations of these sensors are marked on the map, and citizens can click at each location on the map to review how the weather forecast compares with the actual observations in their backyard. The apps we build has the similar functionality. Several tutorials of how to install our sensors and how to use our apps are also hosted in this website for the citizens to access. Progress for evaluating Hyp3. Survey. An electronic survey was conducted in the last summer (right after the project began). In total, ~10 people participated the survey of ~22 multiple-choice questions. We find overall the rural citizens have the desire to know more about technology and information that can lead to better water use for farming. But, they appear to be somewhat conservative to use these technology and information yet. Progress for other hypothesis. Because the proposed smart-and-connected systems have not been fully developed and deployed, we didn't have data to evaluate other hypothesis (except hypothesis 1). Work is in good progress to integrate the crop models into the WRF forecast model and eventually economic model. We have already designed the data flow and software engineering structure to integrate these models.
Publications
- Type:
Websites
Status:
Other
Year Published:
2019
Citation:
High-Plains Real-time Earth System Modeling Complex, http://esmc.uiowa.edu, last access Jan. 2020.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2020
Citation:
- Liang, W., J. Oboamah, X. Qiao, J. Wang, D. Rudnick, H. Yang, X. Cai, Development of a Pilot IoT Network for Irrigation Management and Agricultural Applications in Rural Western Nebraska, ASABE/IA 6th Decennial National Irrigation Symposium, Nov. 30th 2020.
Wang, J., X. Qiao, D. Rudnick, H. Yang, X. Cai, and A. Kasabian
Development of a smart-and-connected irrigation system for rural communities in Nebraska, ASABE/IA 6th Decennial National Irrigation Symposium, Nov. 30th 2020.
- Liang, W., J. Oboamah, X. Qiao, J. Wang, D. Rudnick, H. Yang, X. Cai, A pilot IoT network in rural western Nebraska and some applications, ASABE annual international meeting, July 12th, 2020.
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