Prediction of soil organic matter content based on characteristic band selection method

预处理器 选择(遗传算法) 内容(测量理论) 计算机科学 数据预处理 模式识别(心理学) 特征选择 人工智能 数据挖掘 数学 数学分析
作者
Shugang Xie,Fangjun Ding,Shigeng Chen,Xi Wang,Yuhuan Li,Ke Ma
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:273: 120949-120949 被引量:35
标识
DOI:10.1016/j.saa.2022.120949
摘要

Soil organic matter (SOM) is a key index for evaluating soil fertility and plays a vital role in the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy is an effective method for determining soil properties and is often used to predict SOM content. However, the key prerequisite for effective prediction of SOM content by Vis-NIR spectroscopy lies in the selection of appropriate preprocessing methods and effective data mining techniques. Therefore, in this study, six commonly used spectral preprocessing methods and effective characteristic band selection methods were selected to process the spectrum to predict SOM content. This study aims to determine a stable spectral preprocessing method and explore the predictive performance of different characteristic band selection methods. The results showed that: (i) The first derivative (FD) is the most stable spectral preprocessing method that can effectively improve the spectral characteristic information and the prediction effect of the model. (ii) The prediction effect of SOM content based on characteristic band selection methods is generally better than the full-spectra data. (iii) The precision of FD preprocessing spectrum combined with successive projections algorithm (SPA) in the partial least square regression prediction model of SOM content is the best. (iv) Although the prediction effect of the model based on the optimal band combination algorithm is slightly lower than that of SPA, it shows stable prediction performance, which provides a feasible method for SOM content prediction. In summary, the characteristic band selection method combined with FD can significantly improve the prediction accuracy of SOM content.
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