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A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System
Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696477/ https://www.ncbi.nlm.nih.gov/pubmed/31382683 http://dx.doi.org/10.3390/s19153417 |
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author | Zhu, Longtu Jia, Honglei Chen, Yibing Wang, Qi Li, Mingwei Huang, Dongyan Bai, Yunlong |
author_facet | Zhu, Longtu Jia, Honglei Chen, Yibing Wang, Qi Li, Mingwei Huang, Dongyan Bai, Yunlong |
author_sort | Zhu, Longtu |
collection | PubMed |
description | Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R(2)), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R(2) values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM. |
format | Online Article Text |
id | pubmed-6696477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66964772019-09-05 A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System Zhu, Longtu Jia, Honglei Chen, Yibing Wang, Qi Li, Mingwei Huang, Dongyan Bai, Yunlong Sensors (Basel) Article Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R(2)), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R(2) values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM. MDPI 2019-08-04 /pmc/articles/PMC6696477/ /pubmed/31382683 http://dx.doi.org/10.3390/s19153417 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 Zhu, Longtu Jia, Honglei Chen, Yibing Wang, Qi Li, Mingwei Huang, Dongyan Bai, Yunlong A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System |
title | A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System |
title_full | A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System |
title_fullStr | A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System |
title_full_unstemmed | A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System |
title_short | A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System |
title_sort | novel method for soil organic matter determination by using an artificial olfactory system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696477/ https://www.ncbi.nlm.nih.gov/pubmed/31382683 http://dx.doi.org/10.3390/s19153417 |
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