在冬季小麦育种中增强现象和基因组预测的潜力,使用高通量表型化和深度学习
Swas Kaushal1, Harsimardeep S Gill1, Mohammad Maruf Billah2
1Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States.
Frontiers in plant science
|June 14, 2024
概括
使用无人机和深度学习的高吞吐量表型化改善了小麦的育种. 这种方法提高了谷物产量,试验重量和蛋白质含量的预测准确性,加速了弹性品种的发展.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 遥感 遥感 遥感 遥感
背景情况:
- 加快开发高产,气候适应性小麦品种对于全球粮食安全至关重要.
- 高通量表型 (HTP) 和基因组选择 (GS) 是现代植物育种的关键工具.
- 整合先进的表型化技术可以显著提高繁殖效率.
研究的目的:
- 探索无人机辅助的HTP与深度学习 (DL) 结合用于冬季小麦的现象和多特征基因组预测 (MT-GS) 的使用.
- 使用HTP衍生特征评估谷物产量 (GY),测试重量 (TW) 和谷物蛋白含量 (GPC) 的预测准确性.
- 评估DL和MT-GS模型的有效性,其中包括小麦育种的HTP数据.
主要方法:
- 利用基于无人机的HTP收集不同冬季小麦生长阶段的特征数据.
- 开发并应用深度神经网络 (DNN) 模型用于使用HTP特征进行现象预测.
- 在多特征基因组选择 (MT-GS) 模型中,纳入HTP衍生的植被指数 (ARI,GCI,RVI_2) 作为共变量.
- 使用单位和多位数据集的验证预测模型,包括先进和初步育种线.
主要成果:
- 在农学特征和基于HTP的特征之间发现了显著的相关性.
- 在单个位置上,DNN模型实现了GY (R2=0.71),TW (R2=0.62) 和GPC (R2=0.49) 的强大预测准确度.
- 预测准确度增加了多个位置的数据 (GY:R2=0.76,TW:R2=0.64,GPC:R2=0.75).
- 使用多位置DNN模型,GY的前预测准确度提高了32%.
- 结合HTP特征的MT-GS模型显示,与单一特征模型 (0.23) 相比,GY (0.40) 的预测能力更高.
结论:
- 无人机辅助的HTP与DL模型相结合,显示出在冬季小麦中准确的现象预测的巨大潜力.
- 将HTP特征集成到MT-GS模型中显著提高了关键农学特征的预测能力.
- 这种综合方法可以加快高产和气候适应性小麦品种的育种.
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