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使用数据挖掘算法,研究影响豆 (Pisum arvense L.) 新鲜草产量的因素

Muhammed İkbal Çatal¹, Şenol Çelik², Adil Bakoğlu³

¹Department of Field Crops, Faculty of Agriculture, University of Recep Tayyip Erdogan, Rize, Türkiye.

Frontiers in plant science

|December 5, 2024

概括

多变量自适应回归线 (MARS) 模型最好预测豆植物新鲜草的产量. 这种数据挖掘方法使用基因型和营养含量等因素准确量化产量.

科学领域:

农业科学农业科学
数据挖掘数据挖掘
植物生理学植物生理学

背景情况:

准确预测豆植物新鲜草产量对于农业管理至关重要.
了解影响产量的因素,如营养含量和基因型,至关重要.
数据挖掘算法为模拟复杂的生物系统提供了强大的工具.

研究的目的:

确定影响豆植物湿草产量的关键因素.
为了比较各种数据挖掘算法的预测性能,用于产量估计.
确定最适合的数据挖掘模型来量化新鲜草产量.

主要方法:

使用多变量自适应回归线 (MARS),奇方形自动相互作用检测 (CHAID),分类和回归树 (CART) 和人工神经网络 (ANN) 分析了豆植物产量数据.
模型性能使用适合性标准进行评估,包括R平方,调整R平方,RMSE,MAPE,SD比率,AIC和AICc.
关键的预测参数包括基因型,原蛋白,原灰,酸性洗剂纤维 (ADF) 和中性洗剂纤维 (NDF).

主要成果:

马尔斯方法表现出卓越的预测能力,具有最高的R平方 (0.998) 和调整的R平方 (0.986) 值.
火星实现了最低的错误指标:RMSE (10.499),MAPE (0.7365),和SD比率 (0.047),以及最低的AIC (268) 和AICc (688).
该研究确定了基因型和营养成分作为影响豆植物新鲜草产量的重要因素.

结论:

多变量自适应回归线 (MARS) 是最有效的数据挖掘模型,用于量化豆植物新鲜草产量.
马斯为农业产量预测提供了其他数据挖掘技术的强大而准确的替代方案.
这项研究为通过数据驱动的方法优化豆种植提供了宝贵的见解.

关键词:

一个年龄,一个年龄.汽车车上的车辆.查伊德 (CHAID) 的意思对于MARS算法来说,这是一个非常好的算法.在PEA中,PEA是PEA.

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