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用不同的机器学习算法对日杂交选择进行石油产量预测,使用不同的机器学习算法
Sandra Cvejić1, Olivera Hrnjaković2, Milan Jocković3
1Institute of Field and Vegetable Crops, Novi Sad, Serbia. sandra.cvejic@ifvcns.ns.ac.rs.
Scientific reports
|October 17, 2023
概括
机器学习模型可以准确预测向日油产量,识别疾病耐药性和成熟度等关键特征. 随机森林回归算法在向日繁殖和基因型选择中被证明是最有效的.
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:
- 全球日油需求的增加迫使开发出高产的杂交品种.
- 太阳花油产量预测 (SOYP) 帮助育种者使用先进技术识别优越的杂交品种.
- 机器学习 (ML) 为准确的SOYP提供了一个有希望的方法.
研究的目的:
- 开发和比较ML模型来预测向日油产量.
- 为了确定最相关的特征准确的SOYP.
- 评估 ML 在向日繁殖计划中的潜力.
主要方法:
- 开发并比较了四个ML算法:人工神经网络 (ANN),支持向量回归,K-最近邻居和随机森林回归器 (RFR).
- 利用了1250种日杂交的数据集,其中70%用于培训,30%用于测试.
- 通过使用平均绝对误差 (MAE),平均平方误差 (MSE),根平均平方误差 (RMSE) 和R平方 (R2) 度量来评估模型性能.
主要成果:
- 随机森林回归器 (RFR) 始终优于其他模型,在2019年实现了0.92的R2.
- 人工神经网络 (ANN) 在2018年记录了最低的MAE (65).
- SOYP的主要预测因素包括种子产量,抗扫和菌的耐药性,成熟度和局部性 (受天气影响).
结论:
- ML,特别是RFR,显示了向日油产量预测和基因型选择的巨大潜力.
- 结合疾病耐药性和成熟度等特征可以提高预测的准确性.
- 局部是SOYP的一个有价值的特征,尽管它的有效性取决于天气.
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