使用机器学习将遗传和环境数据结合起来,在多环境试验中预测玉米谷物产量
Igor K Fernandes1, Caio C Vieira2, Kaio O G Dias3
1Department of Crop, Soil, and Environmental Sciences, Center for Agricultural Data Analytics, University of Arkansas, Fayetteville, AR, USA.
概括
机器学习模型通过结合环境数据改善了玉米谷物产量预测,提高了高达7%的准确性. 结合遗传和环境数据 (G+E) 证明比直接建模基因型与环境相互作用 (GEI) 更有效.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 机器学习 机器学习
背景情况:
- 基因组预测模型对于作物育种至关重要.
- 整合环境数据可以提高预测准确度.
- 了解基因型与环境相互作用 (GEI) 对于优化作物性能至关重要.
研究的目的:
- 探索新的机器学习方法,将非遗传 (环境) 信息结合到基因组预测模型中.
- 评估特征工程环境数据在提高玉米谷物产量预测准确性的有效性.
- 为了比较用于整合遗传和环境因素的增量 (G+E) 和乘法 (GEI) 建模策略.
主要方法:
- 利用了来自Genomes To Fields计划的多环境试验数据.
- 开发并比较使用遗传数据,环境数据或其组合的机器学习模型.
- 实施增量 (G+E) 和乘法 (GEI) 的数据集成方法.
- 采用特征工程来处理高维环境数据 (气候,土壤).
主要成果:
- 结合环境数据的机器学习模型,与标准模型相比,平均预测精度提高了高达7%.
- 添加式G+E模型的预测准确度高于或与GEI模型相比较.
- G+E模型在计算效率 (内存和时间) 和灵活性方面提供了优势.
- 特性工程在环境定型和为基因组预测生成有价值的数据方面被证明是有效的.
结论:
- 集成到机器学习模型中的特征工程环境数据有效地捕捉了间接的GEI影响.
- G+E方法是一种灵活有效的策略,用于在育种计划中合并基因型和环境数据.
- 机器学习,特别是特征工程,提供了一个强大的框架,通过利用各种数据源来增强基因组预测.
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