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Estimation of soil organic matter content based on CARS algorithm coupled with random forest by Jinbao Liu; Zhenyu Dong; Junshi Xia; Huanyuan Wang; Tingting Meng; Ruiqing Zhang; Jichang Han; Ni Wang; Jiancang Xie is a Chemistry article available to read on EtoBox.

What is Estimation of soil organic matter content based on CARS algorithm coupled with random forest about?

Soil organic matter (SOM) is an important index used to evaluate soil fertility and nutrient availability, and it is also an important component of precision agriculture. In this study, in order to quickly and efficiently estimate the SOM content of farmland soil, we took 190 farmland soil samples in Jingbian County and measureed the SOM content of the samples in the lab and collected the corresponding Vis-NIR spectroscopy data. Based on the six pretreatment methods, a competitive adaptive weighting algorithm (CARS) is used for characteristic wavelength selection. Random forest (RF) regression is used to establish the predictive SOM model. The results indicate that after the CARS algorithm screens the different spectral variables, the optimal variable sets of the seven spectral variables are 15, 40, 30, 23, 20, 26, and 23, respectively. The accuracy of the model is improved after the CARS algorithm screens the different spectral variables. A total of 15 characteristic variables from the 2151 spectral wavelengths were used as the optimal spectral variable subset; RF shortened the training time required during the SOM modeling process and dramatically improved the model's accuracy an

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Author

Jinbao Liu; Zhenyu Dong; Junshi Xia; Huanyuan Wang; Tingting Meng; Ruiqing Zhang; Jichang Han; Ni Wang; Jiancang Xie

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Chemistry (Physical Sciences)