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考虑到空间异质性的土壤总含量和pH值估计方法:基于GNNW-XGBoost模型.

Hao Liang1, Yue Song2, Zhen Dai3

  • 1College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300 China; Institute of Modern Agriculture and Health Care Industry, Wencheng 325300 China; College of Engineering, China Agricultural University, Beijing 100083 China; Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou 310058,China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 18, 2025
PubMed
概括

一个新的地理神经网络加权-eXtreme梯度增强 (GNNW-XGBoost) 模型准确地估计了土壤的和pH值. 这种方法通过考虑空间变化来改善预测,这对于可持续农业和环境监测至关重要.

关键词:
在GNNW-XGBoost模型中.土壤的pH值和总N值.空间的非静止性是空间的非静止性.在Vis-NIR数据.

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