Dear Colleagues,

We are with Iowa State University in the group of Prof. Ratnesh Kumar engaged in the research on agriculture sensing, modeling and decision-making, as indicated by the list of publications from our group included below.  Also, recently we have contributed cloud-hosted software tools for Agriculture Modeling and Decision-Making software to the MyGeoHub Cloud. These tools can be run from a browser without needing a local install that hopefully will be useful to the user and research communities. 

 

We are trying to reach out to the user communities to inform of our cloud-hosted tools, to seek feedback for future enhancements. It would be of great help if you kindly point to venues (web portals, forums, groups, mailing lists, or researchers) whom we may contact for disseminating the information about our tool. 

I) https://mygeohub.org/tools/rzwqm2 

This is a full-fledged agriculture production and decision-making tool RZWQM2 (Root Zone Water Quality Model 2) ported to cloud for a first time. RZWQM2 is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. Crop simulation options include the generic plant growth model, DSSAT CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm PEST is also added to RZWQM2 for objective model calibration and uncertainty analysis. 

 

II) https://mygeohub.org/tools/benfeddersen 

This tool is a lean soil Nitrogen model that predicts daily soil Nitrate and Ammonia of agriculture field using the User given Inputs of daily soil moisture content, and Nitrate and Ammonia applications. 

                                                                                        

To run the tools, one would visit the links provided. Click the 'Launch Tool' button near top right area of the webpage.  (Sign-in with Mygeohub account or freely create an account if not already holding one.)  

 

References 

Cloud-hosted Modeling and Decision-making: 

[1] A. Bhar, B. Feddersen, R. Malone, and R. Kumar (2021). “Agriculture Model Comparison Framework and MyGeoHub Hosting: Case of Soil Nitrogen”, Inventions, 6(2), 25, Invited Paper. 

This paper develops a lean-N model and model-comparison framework to compare it with RZWQM. Having a lean model helps run a quick optimization that then serves as the seed point for full model optimization.

[2] A. Bhar, R. Kumar, Z. Qi, and R. Malone (2020). “Coordinate descent based agricultural model calibration and optimized input management”, Computers and Electronics in Agriculture, 172, 105353. 

This paper uses real sensor data to calibrate 59 different parameters of RZWQM and uses that for optimization decisions for irrigation and fertilization. Currently the RZWQM modeling is hosted on MyGeoHub. 

 

Plant biostress sensing (electrochemical): 

[3] B. Kashyap and R. Kumar (2021). “A Plug-and-Play type Field-deployable Bio-agent-free Salicylic Acid Sensing System",  IEEE Sensors, 21(21), 24820-24828. This paper devices a portable electrochemical sensor to measure SA, one of the plant health indicator hormones. 

 

Agriculture emitted VOC sensing (optical): 

[4] S. Tabassum, D. P. Kumar, and R. Kumar (2021). “Copper Complex-coated Nanopatterned Fiber-tip Guided Mode Resonance Device for Selective Detection of Ethylene”, IEEE Sensors, 21(16), 17420-17429. 

[5] S. Tabassum, R. Kumar, and L. Dong (2017). “Nanopatterned Optical Fiber Tip for Guided Mode Resonance and Application to Gas Sensing”, IEEE Sensors Journal, 17(22), 7262–7272.

[6] S. Tabassum, R. Kumar, and L. Dong (2017). “Plasmonic Crystal-Based Gas Sensor Toward an Optical Nose Design”.  IEEE Sensors Journal,17(19), 6210–6223. 

[7] S. Tabassum, L. Dong and R. Kumar (2020). “Nano-patterning Methods Including: (1) Patterning of Nanophotonic Structures at Optical Fiber Tip For Refractive Index Sensing and (2) Plasmonic Crystal Incorporating Graphene Oxide Gas Sensor for Detection of Volatile Organic Compound", U.S.Patent No. 10,725,373, July 28, 2020. 

These papers and patent report optical sensors for detecting agriculture emitted volatile organic compounds that are plant health indicators or Green Houses Gases. 

 

Soil nutrient sensing (Microfluidic Impedimetric and Electrophoretic): 

[8] M. A. Ali, H. Jiang, N. K. Mahal, R. J. Weber, R. Kumar, M. J. Castellano, and L. Dong (2017). “Microfluidic impedimetric sensor for soil nitrate detection using graphene oxide and conductive nanofibers enabled sensing interface”, Sensors and Actuators B: Chemical 239, 1289–1299. 

[9] Z. Xu, X. Wang, R. J. Weber, R. Kumar, and L. Dong. “Nutrient Sensing Using Chip Scale Electrophoresis and In Situ Soil Solution Extraction”. IEEE Sensors Journal, 17(14), 4330–4339. 

[10] Z. Xu, L. Dong and R. Kumar, “Electrophoretic soil nutrient sensor for agriculture", U.S.Patent No. 10,564,122, February 18, 2020. 

The above papers and its patent develop a first in-situ, bioagent-free soil nutrient sensing method. 

 

Soil moisture/salinity sensing (Impedance Spectroscopic): 

[11] G. Pandey, R. E. Weber, and R. Kumar (2018). “Agricultural Cyber-physical System: In-Situ Soil Content Estimation by Inversion Analysis of Dielectric Mixture Model", IEEE Access, 6, 43179-43191, 2018, Invited Paper. [12] G. Pandey, R. Kumar, and R. J. Weber (2014). “A low RF-band impedance spectroscopy-based sensor for in-situ, wireless soil sensing”, IEEE Sensors Journal, 14(6), 1997-2005. 

[13] G. Pandey, R. Kumar, and R. J. Weber (2018). “Low RF-Band Impedance Spectroscopy Based Sensor for In-situ,  Wireless Soil Sensing", U.S. Patent No. 10,073,074, Sept. 11, 2018.  The above paper and its patent develop a first in-situ wireless sensor for soil moisture and salinity using impedance spectroscopy measurements.

 

Soil/Plant sensing Surveys: 

[14] B. Kashyap and R. Kumar (2021). “Sensing methodologies in agriculture for monitoring biotic stress in plants due to pathogens and pests", MDPI Inventions, 6(2), 29, Invited Paper. 

This paper surveys the available sensing technologies for plant biotic stress measurement. 

[15] B. Kashyap, and R. Kumar (2021). “Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring”.  IEEE Access, 9, 14095-14121, Invited Paper. 

This paper surveys available sensing technologies for soil moisture and nutrient sensing. 

 

Soil sensor network and sensor localization: 

[16] H. Sahota and R. Kumar (2021). “Sensor Localization using Time of Arrival measurements in Multimedia and Multi-Path Application of In-Situ Wireless Soil Sensing", MDPI Inventions, 6(1), 16, Invited Paper. 

This paper uses time-of-arrival measurements to estimate the x-y-z positions of sensors nodes embedded underground and also deployed over ground. 

[17] H. Sahota and R. Kumar (2018). “Maximum-Likelihood Sensor Node Localization Using Received Signal Strength in Multimedia with Multipath Characteristics", IEEE Systems Journal, 12(1), 506-515.  

This paper uses received signal strength measurements to estimate the x-y-z positions of sensors nodes embedded underground and deployed over ground. 

[18] H. Sahota, R. Kumar and A. Kamal (2011). “A Wireless Sensor Network for Precision Agriculture and its Performance", Wireless Communications and Mobile Computing, 11(2), 1628-1645.  

This paper develops first multi-media soil-air network for precision agriculture with energy efficient wake-up,  synchronization, scheduling, and routing. 

 

Thank you. 

Balaji Pokuri, Anupam Bhar, Ratnesh Kumar 

https://www.ece.iastate.edu/~rkumar/