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. The manuscript of this work is currently under review. 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, UAS）的成像技术在小麦作物倒伏量化中的应用潜力。本数据集包含2016至2017年两个生长季内，于大型小麦育种试验中采集的图像及相关配套文件。基于经验证的数字化倒伏评分，我们对1035份小麦基因型开展了全基因组关联分析（Genome-Wide Association Analysis, GWAS），并在2A染色体上发现了稳定的关联峰值。为进一步验证，我们通过在同一环境及跨环境条件下开展基因组预测交叉验证，检验了小麦倒伏存在复杂遗传架构的假说。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强有力的概念验证依据。本研究相关论文目前处于审稿阶段。本数据集提供了完整的配套文件集合，可用于复现本研究提出的方法与分析流程。