Frank Bai
Bio
Office: Weaver Laboratories 189
Education
B.E. Hydraulic and Hydropower Engineering China Agricultural University 2008
M.E. Agricultural soil and Water Engineering China Agricultural University 2010
Ph.D. Environmental Science and Technology Niigata University, Japan 2014
M.S. Computer Science University of Nebraska-Lincoln 2024
Area(s) of Expertise
Agricultural production is fundamental to human civilization and must continually evolve to become more productive, efficient, affordable, and sustainable. Despite substantial progress, critical technology gaps remain in agricultural sensing, data processing, decision-making, and field implementation. The BAE BAI Lab develops advanced, reliable technologies for precision and digital agriculture, with applications in both crop and livestock production. The lab’s overarching goal is to engineer human-in-the-loop autonomous systems that integrate sensing, artificial intelligence, robotics, and data-driven decision-making to improve farm profitability, operational efficiency, and sustainability. Dr. Bai’s research is inherently interdisciplinary. His publications reflect close collaboration among engineers, agricultural scientists, animal scientists, plant scientists, and producers, demonstrating the importance of combining diverse expertise to develop practical and innovative solutions for agriculture.
Publications
- Assessment of soybean response to irrigation variability in eastern Nebraska using the AquaCrop model , Agricultural Water Management (2026)
- Toward Autonomous Crop Sensing: High-Frequency UAV-Based RGB and Thermal Imaging of Maize and Soybean , Research Square (2026)
- Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping , Sensors (2024)
- Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models , Frontiers in Plant Science (2024)
- High-throughput physiological phenotyping of crop evapotranspiration at the plot scale , Field Crops Research (2024)
- Up-regulation of non-photochemical quenching improves water use efficiency and reduces whole-plant water consumption under drought in Nicotiana tabacum , Journal of Experimental Botany (2024)
- AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge , Computers and Electronics in Agriculture (2023)
- Crop Stress Sensing and Plant Phenotyping Systems: A Review , Smart Agriculture (2023)
- Diurnal Variation of Canopy NDVI in Maize and Soybean , Authorea Preprints (2023)
- Estimating crop stomatal conductance from RGB, NIR, and thermal infrared images , Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII (2023)