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机器学习方法可以预测米粒中的 (Cd) 度,并支持区域范围内的土壤管理

Bo-Yang Huang¹, Qi-Xin Lü¹, Zhi-Xian Tang¹

¹Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Fundamental research

|October 21, 2024

概括

大米中的 (Cd) 污染是一个重大问题. 机器学习模型准确地预测谷物Cd水平,确定土壤pH值和水分作为关键因素,并建议用石灰作为安全大米生产的成本有效解决方案.

科学领域:

环境科学环境科学
农业科学农业科学
毒理学毒理学毒理学

背景情况:

大米是 (Cd) 的主要饮食来源,是一种有毒金属.
根据土壤特性,很难预测米粒Cd度的区域变化,这阻碍了有效的土壤管理.
在中国的土壤和大米样本中,很大一部分超出了Cd.的安全限值.

研究的目的:

开发可靠的预测模型,用于区域大米粒 (Cd) 度.
确定影响大米中Cd吸收的关键土壤因素.
评估成本有效的策略,以减轻大米中Cd污染.

主要方法:

对601对土壤和大米样本进行了为期三年的调查.
采用多重线性回归和机器学习 (支持矢量机器,随机森林,反向传播神经网络) 模型.
分析了土壤特性,包括总Cd,与Fe-Mn氧化物结合的Cd,土壤pH,水分含量和可减少的Mn.

主要成果:

谷物Cd与土壤总Cd之间的相关性较差 (R2 < 0.17).
确定了与Fe-Mn氧化物结合的Cd,土壤pH,水分含量和可减少的Mn作为重要的预测因素.
支持向量机模型实现了最高的预测准确性 (R2 = 0.87).

结论:

机器学习模型可靠地预测区域大米粒Cd度.
将土壤化到pH值为6.5是一种具有成本效益的策略,以减少Cd超值.
这些发现支持改善土壤管理,以确保安全的米生产.

关键词:

是的组成部分.食品安全食品安全重金属重金属是一种重金属.机器学习是机器学习.预测模型是一个预测模型.土壤污染土壤污染

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