机器学习分析干旱压力下的玉米种植特征
Lei Zhang1,2, Fulai Zhang1,2, Wentao Du1,2
1College of Agriculture, Tarim University, Alar 843300, China.
Biology
|July 29, 2025
概括
干旱对玉米生长产生了负面影响. 机器学习模型将植物的高度,重量和叶绿素确定为干旱耐受性的关键指标,XGBoost显示出优越的预测能力,用于培育弹性作物.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 生物技术是生物技术.
背景情况:
- 温室气体度的上升增加了全球干旱风险,威胁到作物生产率.
- 玉米 (Zea mays L.) 是一个重要的全球作物,容易受到干旱压力.
- 开发耐旱品种对于粮食安全至关重要.
研究的目的:
- 调查干旱压力对玉米苗木生长和表型形成的影响.
- 评估机器学习模型的预测性能,以确定干旱耐受性特征.
- 为选耐旱玉米和完善育种策略提供基础.
主要方法:
- 在78种玉米杂交品种中,干旱压力是通过在V3阶段保留水10天而引起的.
- 评估的主要表型特征包括植物高度,茎直径,叶绿素含量和根数.
- 机器学习算法 (随机森林,KNN,XGBoost) 用于分析特征和预测干旱耐受性.
主要成果:
- 植物的高度,地面重量和叶绿素含量被确定为干旱压力玉米表型的主要指标.
- XGBoost模型在分类 (AUC = 0.993) 和回归 (R2 = 0.863) 中取得了高性能.
- XGBoost被证明是预测玉米苗木干旱耐受性的最有效模型.
结论:
- 机器学习为选耐旱玉米品种提供了一种可靠和可行的方法.
- 植物高度,重量和叶绿素等表型特征对于评估耐旱性是有价值的.
- 这项研究支持推进精密育种策略,以提高玉米的弹性.
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