Mr. Biquan Zhao Profile

Biquan Zhao

at Univ of Nebraska - Lincoln

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Publications (3)

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Proceedings Article | 4 June 2022 Presentation + Paper

Estimation of plant height using UAS with RTK GNSS technology

Pascal Izere, Biquan Zhao, Yufeng Ge, Yeyin Shi

Proceedings Volume 12114, 121140P (2022) https://doi.org/10.1117/12.2623033

KEYWORDS: Satellite navigation systems, Calibration, Data modeling, Agriculture, 3D modeling, Clouds, Matrices, Kinematics, Global Positioning System, Tolerancing

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Plant height is an important trait in crop breeding as it has high correlations with grain yield, biomass, and stress tolerance. Most of the studies so far for field-based high-throughput plant height phenotyping with UAS rely on ground control points (GCPs) for geometric calibrations due to the limited positioning accuracy of the GPS onboard. Setting up these GCPs is labor-intensive and is not feasible for a large field. The advent of commercial UAS equipped with Real-Time Kinematic (RTK) Global navigation satellite system (GNSS) technology are expected to achieve a centimeter-level positioning accuracy and have the potential of expediting and improving plant height phenotyping process. Hence, the objective of our study was to investigate the potential and dependency on GCPS of RTK GNSS enabled UAS technology and compare its performance to regular differential GNSS on plant height estimation. In the summer of 2021, images were corrected with two UAS – one with RTK GNSS; and the other with regular differential GNSS – for three different methods over cornfields: Method 1 used both the differential GNSS and the GCPs; Method 2 used both the RTK GNSS with the realtime corrections via cellular network and the GCPs; Method 3 used only the RTK GNSS with the real time corrections via cellular network but without GCPs. In this study, method 2 and 3 resulted with close accuracies, with a 𝑅² of 0.775 for method 2 and a 𝑅²of 0.760 for method 3 in maize plant height estimation. Method 1 had the lowest correlation (𝑅² = 0.250) in this study mostly due to the less data and data quality issues. Results from the study showed that the RTK GNSS enabled UAS has the potential and less dependency on GCPs in deriving accurate horizontal positioning and estimating plant height. Aerial surveys and plant phenotyping with RTK GNSS enabled UAS are more convenient and easily deployed in plant phenotyping and precision agriculture.

Proceedings Article | 12 April 2021 Presentation + Paper

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

Lin Wang, Jiating Li, Lin Zhao, Biquan Zhao, Geng Bai, Yufeng Ge, Yeyin Shi

Proceedings Volume 11747, 1174703 (2021) https://doi.org/10.1117/12.2586694

KEYWORDS: Neural networks, Thermography, Thermal modeling, Feature extraction, Photosynthesis, Neurons, Infrared radiation, Infrared imaging, Image processing, Image analysis

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Leaf area index (LAI) is an important phenotypic trait closely related to plant vigor and biomass. It is also a key parameter used in crop growth modeling. However, manually measuring LAI in the field can be slow and labor intensive. High resolution remote sensing, such as unmanned aircraft systems (UAS), has been explored for LAI estimation but with limited data sources, usually RGB and multispectral imagery. As UAS-based thermal infrared (TIR) imaging becoming readily available in agriculture, it is worth investigating the potential of its role in improving LAI estimation. In this study we evaluated the importance of canopy temperature measured by UAS-based TIR and multispectral imagery on maize LAI quantification within a breeding context (23 genotypes). Five plot-level features (canopy temperature, structure and two common vegetation indices) were extracted from the images, and used as inputs of machine learning models for the LAI estimation. The performance of the estimation was evaluated with a 5-fold cross validation with 30 random repeats for 162 samples. Results showed that, canopy temperature, together with canopy structure as model predictors, slightly improved LAI estimation (root mean square error, RMSE of 0.853 m²/m² and coefficient of determination, R² of 0.740) than those models without temperature difference (RMSE of 0.917 m²/m² and R²of 0.706) for the various genotypes included in this study. In addition, canopy temperature showed moderate and more stable significance in estimating LAI than plant height and image uniformity. Its contribution to the estimation was comparable or even higher than those from vegetation indices when being modeled with random forest in this study. These relationships may be changed with a single or less genotypes which can be explored in future studies.

Proceedings Article | 23 April 2020 Presentation + Paper

Positioning accuracy assessment of a commercial RTK UAS

Biquan Zhao, Jiating Li, Lin Wang, Yeyin Shi

Proceedings Volume 11414, 1141409 (2020) https://doi.org/10.1117/12.2557899

KEYWORDS: Satellite navigation systems, Agriculture, Unmanned aerial vehicles, Accuracy assessment, Remote sensing, Kinematics, Manufacturing, Global Positioning System, RGB color model, Spatial resolution

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Though unmanned aircraft systems (UAS) are widely used in agriculture, their current positioning accuracy in a radius of 0.5 to 2 meters is still too low to pinpoint a crop row or to precisely overlay temporal multi-source field maps together without a valid geometric calibration. The positioning accuracy of UAS deployed with real time kinematic (RTK) global navigation satellite system (GNSS) can be largely increased to a centimeter level, which was claimed from the manufacturers. This paper includes the preliminary test results of positioning accuracy of a commercial RTK UAS over a set of fixed position panels in our customized scenarios. Images were collected in three GNSS modes (regular GNSS without RTK, RTK mode 1 - not corrected by the positioning error of the base station, and RTK mode 2 - corrected by the positioning error of the base station) in static and in-flight settings. In the static setting, horizontal accuracies were 2.17 cm for the RTK mode 2, 12.11 cm for the RTK mode 1, and 11.46 cm for the regular GNSS mode. The significant result of horizontal accuracy in the in-flight setting was that RTK mode 2 without GCPs (2.82 cm) showed comparable accuracy with the commonly used regular GNSS mode with GCPs (1.34 cm). The vertical positioning accuracy in the static setting were 6.01 cm for the RTK mode 2, 5.65 cm for the RTK mode 1, and 10.48 cm for the regular GNSS mode. The accuracy of height measurement from digital surface models (DSMs) without and with GCPs in RTK mode 2 were 4.81 cm and 3.72 cm, respectively, which were the best performance among the three modes. In summary, the RTK UAS tested in this study showed great potential in eliminating the requirement of using GCPs and in high-positioning-accuracy application. The next phase is to test the system in field for accurate crop height measurement at different growth stages in agricultural application.

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