High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV.

Area of Science:

• Agricultural Science
• Remote Sensing
• Bioenergy Crop Research

Background:

• High-throughput plant phenotyping is crucial for advancing bioenergy crops like switchgrass (Panicum virgatum L.).
• Unmanned aerial vehicle (UAV) technology offers a powerful, non-destructive approach for field-based crop assessment.

Purpose of the Study:

• To evaluate UAV-based LiDAR and multispectral imagery for phenotyping field-grown switchgrass.
• To develop a robust model for predicting switchgrass biomass yield using UAV-derived data.
• To assess the utility of UAV remote sensing for switchgrass breeding and cultivar development.

Main Methods:

• Utilized UAV-mounted LiDAR and multispectral sensors for data acquisition.
• Measured plant height, perimeter, and biomass yield, validating with ground truth data.
• Developed a biomass yield model combining canopy height, canopy perimeter, and spectral index.

Main Results:

• UAV-derived plant height and perimeter measurements showed high correlation with manual measurements (r = 0.93).
• Phenotyping parameters improved spectral index saturation for high-density planting detection.
• The developed biomass yield model demonstrated strong correlation with manual biomass measurements (r = 0.90).

Conclusions:

• UAV-based remote sensing is effective for high-throughput phenotyping of switchgrass.
• The integrated UAV data model provides accurate biomass yield predictions.
• This approach supports efficient breeding programs for bioenergy crops.