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使用自动机器学习模型预测作物谷物的重金属度

Ye-Xiang Zhang¹, Feng-Xian Chen², Yu-Hong Zhang¹

¹School of Environmental and Safety Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China.

Ying yong sheng tai xue bao = The journal of applied ecology

|July 3, 2025

概括

自动机器学习模型准确预测作物中的重金属度. 有机肥的使用和植物类型是影响HM积累的关键因素,强调需要控制HM在肥料中的投入.

科学领域:

农业科学农业科学
环境科学环境科学
数据科学数据科学数据科学

背景情况:

由于工业化和密集农业,农作物中的重金属 (HM) 污染越来越令人担忧.
了解农作物中的HM积累对于食品安全和可持续农业实践至关重要.

研究的目的:

使用自动机器学习 (AutoML) 模型预测作物谷物中的HM度.
确定影响作物中HM积累的关键因素.
评估各种机器学习模型对HM预测的性能.

主要方法:

利用来自54个出版物的791个数据集来训练AutoML模型.
输入变量包括土壤特性,肥料特性和植物类型.
输出变量是作物谷物中 (Cr), (Cd), (Pb), (As) 和 (Hg) 的度.
评估深度学习 (DL),梯度增强机 (GBM) 和其他模型.

主要成果:

深度学习 (DL) 模型在预测Cr,Pb,As和Hg方面表现出色.
梯度增强机 (GBM) 实现了Cd预测的最高准确性.
有机肥的应用和植物类型被确定为HM积累的主要驱动因素.
发现与有机肥的应用,土壤HM度和沙子含量的正相关性.
观察到的负相关性与阴离子交换能力,pH值,有机物质和粘土含量.

结论:

DL和GBM模型在预测作物谷物HM度方面表现出卓越的性能.
严格控制有机肥料中的HM投入对于减轻农业风险至关重要.

关键词:

谷物谷物谷物谷物这是一种重金属,重金属.机器学习是机器学习.有机肥有机肥有机肥预测预测预测预测

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