Salas Fernandez Maria G, Bao Yin, Tang Lie, Schnable Patrick S
Department of Agronomy, Iowa State University, Ames, Iowa 50011
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa 50011.
Plant Physiol. 2017 Aug;174(4):2008-2022. doi: 10.1104/pp.17.00707. Epub 2017 Jun 15.
Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective, and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short-stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum (). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed, and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included the following: (1) User-interactive Individual Plant Height Extraction (UsIn-PHe) based on dense stereo three-dimensional reconstruction; (2) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; (3) User-interactive Dense Stereo Matching Stem Diameter Extraction; and (4) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height, while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species.
组学技术的最新进展并未伴随着剖析复杂性状遗传结构所需的同等高效、经济高效且准确的表型分析方法。尽管已针对可控环境开发了高通量表型分析平台，但基于田间的航空和地面技术仅针对矮秆作物进行了设计和部署。因此，我们开发并测试了Phenobot 1.0，这是一种用于高粱等高密植冠层作物的自动转向和自行推进的基于田间的高通量表型分析平台。Phenobot 1.0配备了横向定位和垂直堆叠的立体RGB相机。从307个不同高粱品系收集的图像被重建为三维以进行特征提取。开发了用户界面，并评估了多种算法在估计株高和茎直径方面的准确性。测试的特征提取方法包括：(1)基于密集立体三维重建的用户交互式单株株高提取(UsIn-PHe)；(2)基于密集立体三维重建的自动基于树篱的株高提取(Auto-PHe)；(3)用户交互式密集立体匹配茎直径提取；以及(4)用户交互式图像块立体匹配茎直径提取(IPaS-Di)。比较全基因组关联分析和地面真值验证表明，UsIn-PHe和Auto-PHe都是估计株高的准确方法，而Auto-PHe还具有完全自动化的额外优势。对于茎直径，IPaS-Di对这种与生物量相关的结构性状产生了最准确的估计。总之，我们的技术被证明在获取高粱这种高大密植作物品种基于地面的高通量植株结构参数方面是稳健的。
