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Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (v...

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Autores principales: Yang, Meihua, Xu, Dongyun, Chen, Songchao, Li, Hongyi, Shi, Zhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359233/
https://www.ncbi.nlm.nih.gov/pubmed/30641879
http://dx.doi.org/10.3390/s19020263
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author Yang, Meihua
Xu, Dongyun
Chen, Songchao
Li, Hongyi
Shi, Zhou
author_facet Yang, Meihua
Xu, Dongyun
Chen, Songchao
Li, Hongyi
Shi, Zhou
author_sort Yang, Meihua
collection PubMed
description Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R(2)), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R(2) = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R(2) = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R(2) = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R(2) = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R(2) = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R(2) = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R(2) = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R(2) = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.
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spelling pubmed-63592332019-02-06 Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra Yang, Meihua Xu, Dongyun Chen, Songchao Li, Hongyi Shi, Zhou Sensors (Basel) Article Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R(2)), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R(2) = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R(2) = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R(2) = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R(2) = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R(2) = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R(2) = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R(2) = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R(2) = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain. MDPI 2019-01-11 /pmc/articles/PMC6359233/ /pubmed/30641879 http://dx.doi.org/10.3390/s19020263 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Meihua
Xu, Dongyun
Chen, Songchao
Li, Hongyi
Shi, Zhou
Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
title Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
title_full Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
title_fullStr Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
title_full_unstemmed Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
title_short Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
title_sort evaluation of machine learning approaches to predict soil organic matter and ph using vis-nir spectra
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359233/
https://www.ncbi.nlm.nih.gov/pubmed/30641879
http://dx.doi.org/10.3390/s19020263
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