种子质量推动埃塞俄比亚和塞内加尔的谷物产量:机器学习的见解
Ezekiel Ahn1, Louis K Prom2, Jae Hee Jang1
1Sustainable Perennial Crops Laboratory, Agricultural Research Service, Beltsville Agricultural Research Center, United States of America Department of Agriculture, Beltsville, Maryland, United States of America.
PloS one
|August 14, 2025
概括
预测麦谷物产量是一项挑战. 基于表型的机器学习将种子重量和发芽率确定为关键预测因素,从而实现了改善作物产量的实用早期选择策略.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 计算生物学 计算生物学
背景情况:
- 准确预测麦谷物产量对于作物改善至关重要,特别是在多样化的遗传和地理生殖质中.
- 由于复杂的遗传和环境相互作用,现有的方法在准确预测产量方面面临挑战.
研究的目的:
- 开发和应用一种基于表型的机器学习 (PIML) 框架,用于预测麦谷物产量.
- 确定影响埃塞俄比亚和塞内加尔加入谷物产量的关键现象特征.
主要方法:
- 应用了PIML框架来分析179种加入的9种表型特征.
- 层次聚类和ADASYN过量采样用于表型组分配,达到0.99准确度.
- 一个神经增强模型 (NTanH(3) NBoost(8) 用于谷物产量预测.
主要成果:
- 神经增强模型实现了谷物产量预测的平均R2为0.36和RASE为4.87.
- 种子重量和发芽率始终被确定为谷物产量的最重要的预测因素.
- 抗病特征表明,对谷物产量的预测价值有限.
结论:
- 基于种子质量特征的早期选择,如种子重量和发芽率,可以成为提高果产量的实际策略.
- PIML框架为种育种计划提供了有价值的工具,特别是在资源有限的环境中.
- 这些发现有助于制定更有效的育种策略,以改善全球生产.
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