Frank Bai

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, 24(11), 3420.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Frontiers in Plant Science, 14.

High-throughput physiological phenotyping of crop evapotranspiration at the plot scale

FIELD CROPS RESEARCH, 316.

Up-regulation of non-photochemical quenching improves water use efficiency and reduces whole-plant water consumption under drought in <i>Nicotiana tabacum

Journal of Experimental Botany, 75(13), 3959–3972.

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Computers and Electronics in Agriculture, 215, 108420.

Crop Stress Sensing and Plant Phenotyping Systems: A Review

Smart Agriculture, 5(1), 66–81.

Diurnal Variation of Canopy NDVI in Maize and Soybean

Authorea Preprints.

Estimating crop stomatal conductance from RGB, NIR, and thermal infrared images

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII, 12539, 107–114.

Goniometer in the air: Enabling BRDF measurement of crop canopies using a cable-suspended plant phenotyping platform

Biosystems Engineering, 230, 344–360.

Toward automated irrigation management with integrated crop water stress index and spatial soil water balance

Precision Agriculture, 24(6), 2223–2247.

Ag-IoT for crop and environment monitoring: Past, present, and future

Agricultural Systems, 203, 103497.

Characterization of maize responses to differential nitrogen rates using image-based phenotyping

Authorea Preprints.

Estimating Maize and Soybean Stomatal Conductance Based on Time Series Soil Moisture, Canopy Temperature and Weather Conditions

Authorea Preprints.

Estimating evapotranspiration for crop breeding at the plot scale using energy balance and crop coefficients

AGU Fall Meeting Abstracts, 2022, H25O–1285.

mmWave on a Farm: Channel Modeling for Wireless Agricultural Networks at Broadband Millimeter-Wave Frequency

2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 388–396.

A deep convolutional neural network based image processing framework for monitoring the growth of soybean crops

2021 ASABE Annual International Virtual Meeting, 1.

Cable Suspended Large-Scale Field Phenotyping Facility for High-Throughput Phenotyping Research

In High-Throughput Crop Phenotyping (pp. 39–53). Springer International Publishing Cham.

Crop Sensing and Its Application in Precision Agriculture and Crop Phenotyping

Fundamentals of Agricultural and Field Robotics, 137–155.

Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate

Agricultural Water Management, 256, 107061.

Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI, 11747, 1174703.

Sensor-based irrigation of maize and soybean in East-Central Nebraska under a sub-humid climate

2021 ASABE Annual International Virtual Meeting, 1.

Toward accurate estimating of crop leaf stomatal conductance combining thermal IR imaging, weather variables, and machine learning

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI, 11747, 98–105.

Capturing Spatial Variability in Maize and Soybean using Stationary Sensor Nodes

Differences in soil water changes and canopy temperature under varying water × nitrogen sufficiency for maize

Irrigation Science, 38(5-6), 519–534.

Soil structure and texture effects on the precision of soil water content measurements with a capacitance-based electromagnetic sensor

Transactions of the ASABE, 63(1), 141–152.

Within-Field Variability in Granular Matrix Sensor Data and its Implications for Irrigation Scheduling

Applied Engineering in Agriculture, 36(4), 437–449.

Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery

Scientific Reports, 9(1), 14089.

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.

Capturing diurnal variation of phenotypic traits for breeding plots using NU-Spidercam

AGU Fall Meeting 2018, Dec 10-14, 2018, Washington D.C., USA.

Field-Based Scoring of Soybean Iron Deficiency Chlorosis Using RGB Imaging and Statistical Learning

Frontiers in Plant Science, 9.

Characterizing Wheat Response to Water Limitation Using Multispectral and Thermal Imaging

Transactions of the ASABE, 60(5), 1457–1466.

High throughput field plant phenotyping facility at University of Nebraska-Lincoln and the first year experience

AGU Fall Meeting Abstracts, 2017, B51A–1771.

A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Computers and Electronics in Agriculture, 128, 181–192.

Estimating fresh biomass of maize plants from their RGB images in greenhouse phenotyping

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 9866, 8–13.

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.

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.

The influence of design parameters on the initial spray characteristics of the high-pressure air inclusion nozzle

Atomization and Sprays, 26(4).

Development and validation of a modified model to simulate the sprinkler water distribution

Computers and Electronics in Agriculture, 111, 38–47.

Rapid determination of cabbage quality using visible and near-infrared spectroscopy

LWT-Food Science and Technology, 59(2), 695–700.

Spray Performance of the High Pressure Air Inclusion Nozzle Used in Japan: In the aspects of the relative spray drift and the influence of key design parameters on spray performance

Niigata University.

Characteristics and classification of Japanese nozzles based on relative spray drift potential

Crop Protection, 46, 88–93.

Nozzle classification system in Japan based on the relative spray drift potential.

Power and Machinery. International Conference of Agricultural Engineering-CIGR-AgEng 2012: agriculture and engineering for a healthier life, Valencia, Spain, 8-12 July 2012. Presented at the CIGR-EurAgEng.

Effect of air drag coefficient on motion and evaporation of water droplet

Shuili Xuebao(Journal of Hydraulic Engineering), 42(4), 448–453.

Influence of droplet kinetic energy flux density from fixed spray-plate sprinklers on soil infiltration, runoff and sediment yield

Biosystems Engineering, 110(2), 213–221.

Measuring Sprinkler Droplet Size with Modified Flour Methodology

Transactions of the Chinese Society for Agricultural Machinery.

Model of droplet dynamics and evaporation for sprinkler irrigation

Biosystems Engineering, 106(4), 440–447.

Discussion on application of large-sized sprinkler irrigation machines in Inner Mongolia Autonomous region

Water Saving Irrigation, 1, 18–21.

Leveraging Proximal Imaging and Environmental Data for High-Resolution ET Modeling at Plot Scale

Bai, G., Barker, B., Scoby, D., Irmak, S., Luck, J. D., Neale, C. M. U., … Ge, Y. AGU23.

Spray Performance of the High Pressure Air Inclusion Nozzle Used in Japan

Bai, G.