Geng (Frank) Bai, Ph.D.
• American Geophysical Union
- Ph.D., Environmental Science and Technology, Niigata University, Japan, 2014
- M.E., Agricultural Soil and Water Engineering, China Agricultural University, China, 2010
- B.E., Hydraulic and Hydro-Power Engineering, China Agricultural University, China, 2008
- 100% Research
Advanced plant phenotyping in field and greenhouse environment
- Precision Agriculture
Geng (Frank) Bai started as a Research Assistant Professor for Biological Systems Engineering in 2018. He previously served as a Postdoctoral Research Associate for the department from 2014-2018.
He is interested in advanced plant phenotyping in field and greenhouse environment and precision agriculture. Working with others within Biological Systems Engineering and across the university, he has published peer-review articles and presented his research achievements at professional conferences.
He grew up in Chengdu city, Sichuan province, China. Before joining BSE, he earned his Ph.D. in Environmental Science and Technology from Niigata University, Japan; his M.E. in Agricultural Soil and Water Engineering from China Agricultural University, China; and his B.E. in Hydraulic and Hydro-Power Engineering from China Agricultural University, China.
- Research on using solar-induced chlorophyll fluorescence (SIF) for plant phenotyping and breeding, ORED, 1/1/2019-12/31/2019, G. Bai. Sponsor amount: $20,000 (60% Bai).
- Towards Pivot Automation with Proximal Sensing for Maize and Soybean in the Great Plains, Irrigation Innovation Consortium (IIC), 1/1/2020-12/31/2020, G. Bai. Sponsor amount: $94,659 (10% Bai).
- Monitoring and Evaluating Salinity Status, Hydrological Interaction, and Vegetation Community for Nebraska's East Saline Wetlands, EPA, 10/1/2019-9/30-2022, G. Bai. Sponsor amount: $150,000 (40% Bai).
Refereed Journal Articles in Past 5 Years
- Bai, G., Ge, Y., Scoby, D., Leavitt, B., Stoerger, V., Kirchgessner, N., ... Awada, T. (2019). NU-Spidercam: A large-scale, cable-driven, integrated sensing, and robotic system for advanced phenotyping, remote sensing, and agronomic research. Computers and Electronics in Agriculture, 160, 71-81.
- Yuan, W., Wijewardance, N.K., Jenkins, S., Bai, G., Ge, Y., Graef, G.L. Early prediction of Soybean traits through color and texture features of canopy RGB imagery. Scientific reports, 9(1), 1-17.
- Bai, G., Jenkins, S., Yuan, W., Graef, G.L., Ge, Y., 2018. Field-based scoring of soybean iron deficiency chlorosis using RGB imaging and statistical learning. Frontiers in Plant Science 9, 1002.
- Bai G., Blecha, S., Ge, Y., Walia, H., Phansak, P., 2017. Characterizing wheat response to water limitation using multispectral and thermal imaging. Transaction of the ASABE 60(5), 1457-1466.
- Ge, Y., Bai, G., Stoerger, V., Schnable, J.C., 2016. Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture 127, 625-632.
- Bai, G., Ge, Y., Hussain, W., Baenziger, P.S., Graef, G., 2016. A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Computers and Electronics in Agriculture 128, 181-192.
- Bai, G., Nakano, K., Ohashi, S., Mizukami, T., Yan, H., Kramchote, S., 2016. The influence of design parameters on the initial spray characteristics of the high-pressure air inclusion nozzle. Atomization and Sprays 26(4), 301-317.
- Li, Y., Bai, G., Yan, H., 2015. Development and validation of a modified model to simulate the sprinkler water distribution. Computers and Electronics in Agriculture 111, 38-47.