Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning...

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Autores principales:	Elizabeth Jeanne Parent, Serge-Étienne Parent, Léon Etienne Parent
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34c6b18c52e64759ac4d8aa980964870
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spelling	oai:doaj.org-article:34c6b18c52e64759ac4d8aa9809648702021-12-02T20:05:00ZDetermining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.1932-620310.1371/journal.pone.0233242https://doaj.org/article/34c6b18c52e64759ac4d8aa9809648702021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0233242https://doaj.org/toc/1932-6203Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84-0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81-0.93) or raw percentages (R2 = 0.76-0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49-0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69-0.92) than 4X (R2 = 0.87-0.95). The MIR (R2 = 0.45-0.80) performed better than NIR (R2 = 0.40-0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86-0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.Elizabeth Jeanne ParentSerge-Étienne ParentLéon Etienne ParentPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0233242 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elizabeth Jeanne Parent Serge-Étienne Parent Léon Etienne Parent Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.
description	Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84-0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81-0.93) or raw percentages (R2 = 0.76-0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49-0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69-0.92) than 4X (R2 = 0.87-0.95). The MIR (R2 = 0.45-0.80) performed better than NIR (R2 = 0.40-0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86-0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.
format	article
author	Elizabeth Jeanne Parent Serge-Étienne Parent Léon Etienne Parent
author_facet	Elizabeth Jeanne Parent Serge-Étienne Parent Léon Etienne Parent
author_sort	Elizabeth Jeanne Parent
title	Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.
title_short	Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.
title_full	Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.
title_fullStr	Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.
title_full_unstemmed	Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.
title_sort	determining soil particle-size distribution from infrared spectra using machine learning predictions: methodology and modeling.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/34c6b18c52e64759ac4d8aa980964870
work_keys_str_mv	AT elizabethjeanneparent determiningsoilparticlesizedistributionfrominfraredspectrausingmachinelearningpredictionsmethodologyandmodeling AT sergeetienneparent determiningsoilparticlesizedistributionfrominfraredspectrausingmachinelearningpredictionsmethodologyandmodeling AT leonetienneparent determiningsoilparticlesizedistributionfrominfraredspectrausingmachinelearningpredictionsmethodologyandmodeling
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Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

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