In the U.S. since 2013, the sugarcane aphid is a perennial pest to all types of sorghum. Rating sugarcane aphid population density, plant damage, and other traits in sorghum requires a large amount of labor and ratings, especially damage ratings, may vary by evaluator. Thus Unmanned Aerial Systems (UAS)–based imagery may be exceedingly useful to more accurately quantify the effects on sorghum caused by sugarcane aphids. This study quantified the dynamic nature of sugarcane aphid infestations on silage sorghum varieties using UAS-based imagery data, and demonstrated the UAS-based measurements correlated to ground measurements. Two UAS platforms equipped with RGB (red, green, and blue) and multispectral cameras respectively were used to evaluate the silage sorghum variety trials during the growing seasons of 2019 and 2020. For the purpose of high throughput phenotyping in sorghum breeding, a new workflow scheme was developed including UAS image processing, raster calculation, DTM (digital terrain model) and CHM (canopy height model) generation, image extraction of sorghum plants, and tabular dataset generation from zonal statistics for further statistical analyses. Ground-based measurements included aphid sampling, aphid damage ratings, plant height, and biomass yields. The normalized difference red edge index (NDRE) and canopy cover collected by the UAS showed negative linear relationship with aphid damage ratings in both trials (R² = 0.55–0.64). In addition to assessing spatial differences among the varieties in 2019, temporal change in both NDRE and canopy cover from the baseline sampling date in 2020 better estimated aphid damage, R² of 0.68 and 0.79 respectively, than using the spatial difference of NDRE (R² = 0.55) and canopy cover (R² = 0.57) before harvest. Plant height (R² = 0.84, Root-Mean-Square Error (RMSE) = 0.16 m) can be estimated with efficiency and precision using UAS-derived measurements during high throughput phenotyping of sorghum. Fresh yield estimates for the primary harvests were consistent in both years, but green yield estimates differed among harvests and need to be improved. Future development of UAS-based high throughput phenotyping would benefit from increased temporal resolutions of growth parameters and vegetation indices throughout a growing season.

自2013年以来，在美国，甘蔗蚜虫是所有类型高粱的多年生害虫。对甘蔗蚜虫种群密度，植物危害和高粱其他性状进行评级需要大量的工作和评级，尤其是损害评级，评估者可能会有所不同。因此，基于无人机系统（UAS）的图像对于更准确地量化甘蔗蚜虫对高粱的影响可能非常有用。这项研究使用基于UAS的图像数据量化了青贮高粱品种上甘蔗蚜虫侵染的动态特性，并证明了基于UAS的测量与地面测量相关。在2019年和2020年的生长季节中，分别使用配备有RGB（红色，绿色和蓝色）和多光谱相机的两个UAS平台评估青贮高粱品种试验。为了在高粱育种中实现高通量表型化，开发了一种新的工作流程方案，包括UAS图像处理，栅格计算，DTM（数字地形模型）和CHM（冠层高度模型）生成，高粱植物的图像提取以及表格数据集的生成从区域统计数据中进行进一步的统计分析。地面测量包括蚜虫采样，蚜虫危害等级，植物高度和生物量产量。在两个试验中，UAS收集的归一化差异红边指数（NDRE）和树冠覆盖与蚜虫危害等级呈负线性关系（R 以及从区域统计数据生成表格数据集以进行进一步的统计分析。地面测量包括蚜虫采样，蚜虫危害等级，植物高度和生物量产量。在两个试验中，UAS收集的归一化差异红边指数（NDRE）和树冠覆盖与蚜虫危害等级呈负线性关系（R 以及从区域统计数据生成表格数据集以进行进一步的统计分析。地面测量包括蚜虫采样，蚜虫危害等级，植物高度和生物量产量。在两个试验中，UAS收集的归一化差异红边指数（NDRE）和树冠覆盖与蚜虫危害等级呈负线性关系（R²  = 0.55-0.64）。除了评估2019年品种之间的空间差异外，从2020年的基准采样日期开始，NDRE和冠层覆盖的时间变化都比使用NDRE（R ^2）更好地估计了蚜虫危害，R ²分别为0.68和0.79。 = 0.55）和 收获前的树冠覆盖度（R ² = 0.57）。株高（R ² = 0.84，在高通量高粱表型分析中，使用UAS衍生的测量方法可以高效，精确地估计均方根误差（RMSE）= 0.16 m）。两年中主要收成的新鲜单产估算值是一致的，但是不同收成中的绿色单产估算值不同，需要加以改进。基于UAS的高通量表型的未来发展将受益于整个生长季节生长参数和植被指数的时间分辨率提高。