褐土全氮含量Vis/NIRS组合预测模型的构建

doi:10.3964/j.issn.1000-0593(2024)08-2310-08

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摘要：准确掌握农田土壤全氮含量对于评估土壤肥力，合理施用氮肥具有重要意义。为综合利用各个单预测模型的优势，提升整体预测性能、降低模型方差，提高鲁棒性，以农田褐土土壤为研究对象，基于近红外-可见光高光谱数据，提出了一种基于标准差的预测有效度组合预测模型（CPM），用于预测土壤全氮含量。对原高光谱数据采用Savitzky-Golay平滑和一阶微分变换，采用树模型进行特征波段提取，利用决策树回归（DTR）（模型1）、高斯核回归（GKR）（模型2）、随机森林回归（RF）（模型3）、LASSO回归（模型4）、多层感知器回归（MLP）（模型5）5个单预测模型，通过单预测模型的线性组合建立组合预测模型。结果表明：(1）通过广义简约梯度优化算法求得组合预测模型中5个单预测模型的权重分别为ω^*₁=0.407，ω^*₂=0.378，ω^*₃=0.215，ω^*₄=0，ω^*₅=0；(2）对于所有数据，基于5种单预测模型和组合预测模型对土壤全氮含量预测的预测有效度分别为M₁=0.855，M₂=0.856，M₃=0.847，M₄=0.785，M₅=0.796，M_CPM=0.880；与单模型预测有效度最大值相比，组合预测模型预测有效度提高了2.924%；(3）对于所有数据，基于5种单预测模型和组合预测模型对土壤全氮含量预测的预测精度和标准差分别为E(A₁)=0.924，σ(A₁)=0.075，E(A₂)=0.928，σ(A₂)=0.077，E(A₃)=0.923，σ(A₃)=0.082，E(A₄)=0.882，σ(A₄)=0.109，E(A₅)=0.889，σ(A₅)=0.104，E(A_CPM)=0.937，σ(A_CPM)=0.066，与单模型预测精度最大值相比，组合预测模型预测精度提高了0.970%，模型稳定性提高了12.000%，且为优性组合预测。组合预测模型可用于可见光-近红外光谱数据的农田褐土土壤全氮含量的有效估测，可为农田土壤全氮含量的快速监测提供依据和参考。

关键词：可见光-近红外；高光谱预测；全氮含量；组合预测模型

Abstract：Accurately grasping the total nitrogen content of farmland soil is significant for evaluating soil fertility and applying nitrogen fertilizer reasonably. To comprehensively utilize the advantages of each single prediction Model, improve the overall prediction performance, reduce the variance of the model, and improve the robustness, this study takes farmland brown soil as the research object, and based on near-infrared and visible hyperspectral data, puts forward a Combined prediction model based on standard deviation. CPM was used to predict soil total nitrogen content. Savitzky-Golay smoothing and first-order differential transformation are applied to the original hyperspectral data, and a tree model is used for feature band extraction. Using five single prediction models, Decision Tree Regression (DTR) (Model 1), Gaussian Kernel Regression (GKR) (Model 2), Random Forest Regression (RF) (Model 3), LASSO Regression (Model 4), and Multi-Layer Perceptron (MLP) (Model 5), a combination prediction model is established through a linear combination of single prediction models. The results indicate that: (1) The weights of the five single prediction models in the combined prediction model are obtained by generalized reduced gradient optimization algorithm: ω^*₁=0.407，ω^*₂=0.378，ω^*₃=0.215，ω^*₄=0，ω^*₅=0; (2) For all data, the predictive effectiveness of five single prediction models and combined prediction models for predicting soil total nitrogen content is M, respectively M₁=0.855，M₂=0.856，M₃=0.847，M₄=0.785，M₅=0.796，M_CPM=0.880, compared to the maximum predictive validity of a single model, the predictive validity of the combination prediction model has increased by 2.924%; (3) For all data, the prediction accuracy and standard deviation of soil total nitrogen content based on five single prediction models and combined prediction models are E(A₁)=0.924，σ(A₁)=0.075，E(A₂)=0.928，σ(A₂)=0.077，E(A₃)=0.923，σ(A₃)=0.082，E(A₄)=0.882，σ(A₄)=0.109，E(A₅)=0.889，σ(A₅)=0.104，E(A_CPM)=0.937，σ(A_CPM)=0.066, compared to the maximum prediction accuracy of a single model, the combination prediction model has improved prediction accuracy by 0.970% and model stability by 12.000%, making it an optimal combination prediction model. The combined prediction model can effectively estimate the total nitrogen content of farmland brown soil based on visible-near-infrared spectral data and can provide a basis and reference for the rapid monitoring of the total nitrogen content of farmland soil.

Key words：Visible-near infrared; Hyperspectral estimation; Total nitrogen content; Combination prediction model

收稿日期: 2023-04-17 修订日期: 2023-09-21

中图分类号:

S153.621

基金资助: 国家重点研发计划子课题（2021YFD1600301-4），中央引导地方科技发展资金项目（YDZJSX20231C009），校学术恢复项目（2023XSHF2）资助

通讯作者: 李志伟 E-mail: lizhiweitong@163.com

作者简介: 张秀全，1980年生，山西农业大学农业工程学院讲师 e-mail: zhangxiuq1980@126.com

引用本文:

张秀全，马世兴，李志伟，郑德聪，宋海燕. 褐土全氮含量Vis/NIRS组合预测模型的构建[J]. 光谱学与光谱分析, 2024, 44(08): 2310-2317.
ZHANG Xiu-quan, MA Shi-xing, LI Zhi-wei, ZHENG De-cong, SONG Hai-yan. Prediction of Total Nitrogen Content in Brown Soil Based on Hyperspectral and Combined Prediction Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2310-2317.

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