Gomez Francisco E, Carvalho Geraldo, Shi Fuhao, Muliana Anastasia H, Rooney William L
1Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695 USA.
2Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA.
Plant Methods. 2018 Jul 13;14:59. doi: 10.1186/s13007-018-0326-3. eCollection 2018.
In bioenergy/forage sorghum, morpho-anatomical stem properties are major components affecting standability and juice yield. However, phenotyping these traits is low-throughput, and has been restricted by the lack of a high-throughput phenotyping platforms that can collect both morphological and anatomical stem properties. X-ray computed tomography (CT) offers a potential solution, but studies using this technology in plants have evaluated limited numbers of genotypes with limited throughput. Here we suggest that using a medical CT might overcome sample size limitations when higher resolution is not needed. Thus, the aim of this study was to develop a practical high-throughput phenotyping and image data processing pipeline that extracts stem morpho-anatomical traits faster, more efficiently and on a larger number of samples.
A medical CT was used to image morpho-anatomical stem properties in sorghum. The platform and image analysis pipeline revealed extensive phenotypic variation for important morpho-anatomical traits in well-characterized sorghum genotypes at suitable repeatability rates. CT estimates were highly predictive of morphological traits and moderately predictive of anatomical traits. The image analysis pipeline also identified genotypes with superior morpho-anatomical traits that were consistent with ground-truth based classification in previous studies. In addition, stem cross section intensity measured by the CT was highly correlated with stem dry-weight density, and can potentially serve as a high-throughput approach to measure stem density in grass stems.
The use of CT on a diverse set of sorghum genotypes with a defined platform and image analysis pipeline was effective at predicting traits such as stem length, diameter, and pithiness ratio at the internode level. High-throughput phenotyping of stem traits using CT appears to be useful and feasible for use in an applied breeding program.
在生物能源/饲用高粱中，茎的形态解剖学特性是影响植株抗倒伏性和汁液产量的主要因素。然而，对这些性状进行表型分析的通量较低，并且一直受到缺乏能够同时收集茎的形态学和解剖学特性的高通量表型分析平台的限制。X射线计算机断层扫描（CT）提供了一种潜在的解决方案，但使用该技术对植物进行的研究评估的基因型数量有限且通量较低。在此我们提出，当不需要高分辨率时，使用医用CT可能会克服样本量的限制。因此，本研究的目的是开发一种实用的高通量表型分析和图像数据处理流程，能够更快、更高效地从大量样本中提取茎的形态解剖学性状。
使用医用CT对高粱茎的形态解剖学特性进行成像。该平台和图像分析流程在适当的重复率下揭示了特征明确的高粱基因型中重要形态解剖学性状的广泛表型变异。CT估计值对形态学性状具有高度预测性，对解剖学性状具有中等预测性。图像分析流程还鉴定出具有优良形态解剖学性状的基因型，这些基因型与先前研究中基于实际情况的分类结果一致。此外，CT测量的茎横截面积强度与茎干重密度高度相关，并且有可能作为一种高通量方法来测量禾本科植物茎的密度。
使用CT结合特定的平台和图像分析流程对多种高粱基因型进行分析，能够有效地预测节间水平的茎长、直径和髓比率等性状。利用CT对茎性状进行高通量表型分析似乎在应用育种计划中是有用且可行的。
