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特性工程和参数调整:在多环境硬小麦育种试验中提高现象预测能力

Carina Meyenberg¹, Vincent Braun¹, Carl Friedrich Horst Longin¹

¹State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik

|July 22, 2024

概括

使用近红外光谱 (NIRS) 在 durum wheat 中优化现象选择显著提高了预测准确性. 在植物育种中,定制数据预处理对于增强特征和模型预测至关重要.

科学领域:

植物育种植物育种
农业科学农业科学
频谱学是一种光谱学.

背景情况:

现象选择利用光谱数据预测植物的表现.
有效的选择对于成功的植物育种计划至关重要.
近红外光谱 (NIRS) 为现象选择提供了一个工具.

研究的目的:

为了优化硬小麦的现象选择.
通过功能工程和参数调整来提高预测能力.
研究基因型和环境对农业和质量特征预测的影响.

主要方法:

在多环境硬小麦试验中应用现象选择.
利用特征工程和参数调整来改善预测.
在Savitzky-Golay过器参数上使用网格搜索来进行预处理优化.

主要成果:

预处理提高了现象预测能力高达1500%.
优化的预处理依赖于数据集,特征和模型.
现象预测显示能力低至中等,在同一个环境内预测时最高.

结论:

在 durum小麦中优化现象选择需要量身定制的NIRS预处理.
功能工程和参数调整增强了预测能力.
NIRS捕获基因型和基因型与环境相互作用变异.

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