Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning
Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total eleme...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2020
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| Acceso en línea: | https://hdl.handle.net/10568/110769 |
| _version_ | 1855514793761308672 |
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| author | Towett, Erick K. Drake, L. B. Acquah, G. E. Haefele, S. M. McGrath, S. P. Shepherd, Keith D. |
| author_browse | Acquah, G. E. Drake, L. B. Haefele, S. M. McGrath, S. P. Shepherd, Keith D. Towett, Erick K. |
| author_facet | Towett, Erick K. Drake, L. B. Acquah, G. E. Haefele, S. M. McGrath, S. P. Shepherd, Keith D. |
| author_sort | Towett, Erick K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total elemental composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments. |
| format | Journal Article |
| id | CGSpace110769 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1107692025-01-24T14:11:56Z Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning Towett, Erick K. Drake, L. B. Acquah, G. E. Haefele, S. M. McGrath, S. P. Shepherd, Keith D. Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total elemental composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments. 2020-12-10 2021-01-08T13:15:58Z 2021-01-08T13:15:58Z Journal Article https://hdl.handle.net/10568/110769 en Open Access Public Library of Science Towett, E. K.; Drake, L. B.; Acquah, G. E.; Haefele, S. M.; McGrath, S. P.; Shepherd, K. D. 2020. Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning. PLoS ONE 15(12): e0242821. https://doi.org/10.1371/journal.pone.0242821 |
| spellingShingle | Towett, Erick K. Drake, L. B. Acquah, G. E. Haefele, S. M. McGrath, S. P. Shepherd, Keith D. Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning |
| title | Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning |
| title_full | Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning |
| title_fullStr | Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning |
| title_full_unstemmed | Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning |
| title_short | Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning |
| title_sort | comprehensive nutrient analysis in agricultural organic amendments through non destructive assays using machine learning |
| url | https://hdl.handle.net/10568/110769 |
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