Unmanned aerial vehicle (UAV) technology is an emerging powerful approach for high-throughput plant phenotyping field-grown crops. Switchgrass (Panicum virgatum L.) is a lignocellulosic bioenergy crop for which studies on yield, sustainability, and biofuel traits are performed. In this study, we exploited UAV-based imagery (LiDAR and multispectral approaches) to measure plant height, perimeter, and biomass yield in field-grown switchgrass in order to make predictions on bioenergy traits. Manual ground truth measurements validated the automated UAV results. We found UAV-based plant height and perimeter measurements were highly correlated and consistent with the manual measurements (r = 0.93, p < 0.001). Furthermore, we found that phenotyping parameters can significantly improve the natural saturation of the spectral index of the optical image for detecting high-density plantings. Combining plant canopy height (CH) and canopy perimeter (CP) parameters with spectral index (SI), we developed a robust and standardized biomass yield model [biomass = (m × SI) × CP × CH] where the m is an SI-sensitive coefficient linearly varying with the plant phenological changing stage. The biomass yield estimates obtained from this model were strongly correlated with manual measurements (r = 0.90, p < 0.001). Taking together, our results provide insights into the capacity of UAV-based remote sensing for switchgrass high-throughput phenotyping in the field, which will be useful for breeding and cultivar development.

无人机（UAV）技术是一种针对田间种植的高通量植物表型鉴定的新兴强大方法。柳枝（（紫阳花L.）是一种木质纤维素生物能源作物，针对其产量，可持续性和生物燃料性状进行了研究。在这项研究中，我们利用基于无人机的图像（LiDAR和多光谱方法）来测量田间生长的柳枝height的植物高度，周长和生物量产量，以便对生物能源性状做出预测。手动地面真相测量验证了自动无人机的结果。我们发现基于无人机的工厂高度和周长测量值高度相关，并且与手动测量值一致（[R = 0.93， p<0.001）。此外，我们发现表型参数可以显着提高用于检测高密度种植的光学图像光谱指数的自然饱和度。结合植物冠层高度（CH）和冠层周长（CP）参数与光谱指数（SI），我们开发了一个强大且标准化的生物量产量模型[biomass =（米 ×SI）×CP×CH]其中 米是SI敏感系数，随植物物候变化阶段线性变化。从该模型获得的生物量产量估算值与人工测量值密切相关（[R = 0.90， p<0.001）。综合起来，我们的结果提供了对基于无人机的遥感在野外柳枝高通量表型分析中的能力的见解，这将对育种和栽培品种开发有用。