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通过使用卫星图像用于精确农业的集合学习技术预测土壤有机碳

Shyamal Mundada¹, Pooja Jain²

¹Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, 441108, India. mundadasg30@gmail.com.

Scientific reports

|August 6, 2025

概括

本研究引入了一种机器学习系统,使用卫星数据和土壤特性来评估土壤有机碳. XGBoost模型实现了高精度,帮助农民精确地应用肥料,以提高作物产量.

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:

土壤成分对农业至关重要,土壤有机碳 (SOC) 对土壤健康和可持续实践至关重要.
准确的SOC评估使得可以做出明智的农业决策,优化作物生长和资源管理.

研究的目的:

开发一种机器学习 (ML) 系统,使用地形,遥感和气候数据评估土壤有机碳 (SOC).
为了比较不同ML算法的性能,包括XGBoost,随机森林和堆叠,用于SOC预测.

主要方法:

利用卫星衍生的地形变量,遥感指数和气候数据作为预测变量.
用土壤健康卡数据作为模型培训和验证的依赖变量.
应用和评估了XGBoost,随机森林和堆叠组合方法用于SOC估计.

主要成果:

在训练期间,XGBoost表现出卓越的性能,具有最高的R平方 (0.95) 和最低的RMSE (0.03).
对于测试数据集,XGBoost和Random Forest显示了不同的性能指标,堆叠有效地减轻了过拟合.
开发的系统旨在提供精确的肥料建议,从而提高作物产量.

结论:

应用于遥感数据的机器学习技术为准确农业中构建决策支持系统提供了强大的方法.
拟议的系统可以显著帮助农民优化农业投入,提高农场的整体生产力.
通过ML精确的SOC监测对于推进可持续农业和确保粮食安全至关重要.

关键词:

数字化土壤分析机器学习是机器学习.多光谱图像是多光谱图像.精准农业是一种精准农业.土壤有机碳土壤有机碳

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