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样本大小和机器学习算法对数字土壤营养物质测绘准确度的影响
Prava Kiran Dash1,2, Caner Ferhatoglu3, Bradley A Miller3
1Department of Soil Science and Agricultural Chemistry, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, 751003, India. pravakirandash@ouat.ac.in.
Environmental monitoring and assessment
|August 8, 2025
概括
增加样本大小可以通过机器学习 (ML) 算法提高土壤营养预测的准确性. 选择最佳样本大小对于在土壤绘图项目中平衡准确性和效率至关重要.
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:
- 准确地绘制土壤营养物质地图对于可持续农业和环境管理至关重要.
- 机器学习 (ML) 为预测土壤特性提供了强大的工具,但性能受数据量的影响.
研究的目的:
- 评估不同样本大小对5个ML算法对14种土壤属性的预测性能的影响.
- 为了比较多层感知器 (MLP),随机森林 (RF),额外树木回归器 (ETR),CatBoost和梯度提升 (GB) 在土壤营养预测中的有效性.
主要方法:
- 在数据集上训练了五个ML算法,样本大小从25到800不等,用于14种土壤属性.
- 利用来自数字地形和Sentinel-2图像的574个环境变量作为预测因素.
- 使用林的一致性相关系数 (CCC) 和根平均平方误差 (RMSE) 评估预测准确性.
主要成果:
- 对于所有ML算法,预测准确性通常随样本大小的增加而增加,在某个特定点之后,回报率下降.
- 射频,ETR,CatBoost和GB的表现优于MLP,在各种土壤属性中显示出更好的预测性能.
- 微量营养素显示,随着样本大小的增加,预测准确度显著改善.
结论:
- 可以确定最佳样本大小,以高效地实现准确的土壤营养预测.
- 选择适当的ML算法以及最佳样本大小是最大限度地提高土壤绘制预测准确性的关键.
- 该研究为土壤调查和绘图项目的资源分配提供了指导.
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