Background In the early stages of plant breeding programs high-quality phenotypes are still a constraint to improve genetic gain. New field-based high-throughput phenotyping (HTP) platforms have the capacity to rapidly assess thousands of plots in a field with high spatial and temporal resolution, with the potential to measure secondary traits correlated to yield throughout the growing season. These secondary traits may be key to select more time and most efficiently soybean lines with high yield potential. Soybean average canopy coverage (ACC), measured by unmanned aerial systems (UAS), is highly heritable, with a high genetic correlation with yield. The objective of this study was to compare the direct selection for yield with indirect selection using ACC and using ACC as a covariate in the yield prediction model (Yield|ACC) in early stages of soybean breeding. In 2015 and 2016 we grew progeny rows (PR) and collected yield and days to maturity (R8) in a typical way and canopy coverage using a UAS carrying an RGB camera. The best soybean lines were then selected with three parameters, Yield, ACC and Yield|ACC, and advanced to preliminary yield trials (PYT). Results We found that for the PYT in 2016, after adjusting yield for R8, there was no significant difference among the mean performances of the lines selected based on ACC and Yield. In the PYT in 2017 we found that the highest yield mean was from the lines directly selected for yield, but it may be due to environmental constraints in the canopy growth. Our results indicated that PR selection using Yield|ACC selected the most top-ranking lines in advanced yield trials. Conclusions Our findings emphasize the value of aerial HTP platforms for early stages of plant breeding. Though ACC selection did not result in the best performance lines in the second year of selections, our results indicate that ACC has a role in the effective selection of high-yielding soybean lines.

中文翻译：

---

通过高通量冠层表型分析提高大豆育种效率。


背景 在植物育种计划的早期阶段，高质量表型仍然是提高遗传增益的制约因素。新的基于田间的高通量表型分析（HTP）平台能够以高空间和时间分辨率快速评估田地中的数千个地块，并有可能测量与整个生长季节产量相关的次要性状。这些次要性状可能是选择时间更长、效率最高、具有高产潜力的大豆品系的关键。通过无人机系统（UAS）测量的大豆平均冠层覆盖度（ACC）具有高度遗传性，与产量具有高度遗传相关性。本研究的目的是比较大豆育种早期阶段产量预测模型 (Yield|ACC) 中使用 ACC 的直接选择与间接选择以及使用 ACC 作为协变量的产量。 2015 年和 2016 年，我们种植后代行 (PR)，并以典型方式收集产量和成熟天数 (R8)，并使用携带 RGB 相机的 UAS 收集树冠覆盖范围。然后通过三个参数（产量、ACC 和产量 | ACC）选择最佳大豆品系，并进入初步产量试验 (PYT)。结果我们发现，对于2016年的PYT，在调整R8的产量后，根据ACC和产量选择的品系的平均性能没有显着差异。在2017年的PYT中，我们发现产量平均值最高的是直接选择产量的品系，但这可能是由于冠层生长的环境限制所致。我们的结果表明，使用 Yield|ACC 的 PR 选择在高级产量试验中选择了排名最高的品系。结论 我们的研究结果强调了空中 HTP 平台对于植物育种早期阶段的价值。 尽管 ACC 选择在第二年的选择中并未产生最佳性能品系，但我们的结果表明 ACC 在高产大豆品系的有效选择中发挥着作用。