预测向日产量的先进计算方法:在正常和盐度压力环境中,ANN,ANFIS和GEP的洞察力
Sanaz Khalifani1, Reza Darvishzadeh1, Majid Montaseri2
1Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
PloS one
|February 24, 2025
概括
基因表达编程 (GEP) 准确地预测了在正常和盐度压力下向日的谷物产量. 在压力下影响产量的关键因素包括叶子长度,头径,百种子重量和头干重量.
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:
- 准确的作物产量预测对于粮食安全和农民决策至关重要.
- 太阳花的产量受各种形态特征和环境条件的影响,包括盐度压力.
- 先进的计算模型为精确的产量预测提供了潜力.
研究的目的:
- 在正常和盐度压力条件下预测向日谷粒产量.
- 为了比较人工神经网络 (ANN),自适应神经模糊推理系统 (ANFIS) 和基因表达编程 (GEP) 的预测性能.
- 在盐度压力下确定影响向日产量的关键形态特征.
主要方法:
- 在一个生长季节使用96个近亲生日线进行了盆栽实验.
- 测量了10个形态特征作为产量预测的输入变量.
- 使用NaCl诱导盐度应激,其电导率水平为2dS/m (控制) 和8dS/m (应激).
- 用ANN,ANFIS和GEP模型来预测谷物产量.
主要成果:
- GEP模型在正常条件和盐度压力条件下显示了向日谷粒产量的最高预测准确度.
- 与其他模型相比,GEP的R2值为0.803 (正常) 和0.743 (压力),RMSE和MAE较低.
- 灵敏度分析显示,叶子长度,头径,百种子重量和头干重量是盐度压力下产量的关键因素.
结论:
- 基因表达编程 (GEP) 是一个非常有效的工具,用于预测向日的谷物产量.
- 在各种环境条件下,特别是盐度压力下,GEP模型可以帮助制定改善日产量的策略.
- 识别关键的形态特征可以指导育种计划,以提高盐度耐受性和产量稳定性.
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