Full dataset for high-throughput phenotyping-enabled genetic dissection of crop lodging in wheat.

The primary objective of this research was to evaluate the potential of unmanned aerial systems-based imaging for quantification of crop lodging in wheat. This dataset contains images and associated files collected from large wheat breeding trials over the course of two seasons in 2016 and 2017. Using the validated digital lodging scores, we performed Genome-Wide Assocation Analysis on 1035 wheat genotypes and found a consistent peak at chromosome 2A. To follow up, we tested the hypothesis of complex genetic architecture of lodging in wheat by applying genomic prediction cross-validations within and across environments. Our findings provide a strong proof-of-concept in support of the application of unmanned aerial systems in breeding and genetic studies. This work was published in Frontiers in Plant Science (2019). This dataset provides a comprehensive collection of files to replicate the proposed methods and analysis. <br>

本研究的核心目标为评估基于无人机系统（Unmanned Aerial Systems）成像技术对小麦作物倒伏进行量化分析的潜力。本数据集包含2016年与2017年两个生长季内，于大规模小麦育种试验中采集的图像及配套关联文件。本研究借助经验证的数字化倒伏评分数据，对1035份小麦基因型开展全基因组关联分析（Genome-Wide Association Analysis），并在2A号染色体上检测到稳定的显著性峰值。为进一步验证相关假说，本研究通过在环境内及跨环境场景下开展基因组预测交叉验证，对小麦倒伏存在复杂遗传结构的假设进行了检验。本研究结果为无人机系统在育种及遗传研究中的应用提供了坚实的概念验证依据。该项研究成果已于2019年发表于《植物科学前沿》（Frontiers in Plant Science）期刊。本数据集提供了完整的配套文件集，可用于复现本文所提出的研究方法与分析流程。