Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | LOCUS Repositório Institucional da UFV |
Texto Completo: | https://locus.ufv.br//handle/123456789/29841 |
Resumo: | Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000–2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00–0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC. |
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Mendes, Wanderson de SousaBoechat, Cácio LuizGualberto, Adriano Venicius SantanaBarbosa, Ronny SobreiraSilva, Yuri Jacques Agra Bezerra daSaraiva, Paloma CunhaSena, Antonny Francisco Sampaio deDuarte, Lizandra de Sousa Luz2022-09-05T12:48:29Z2022-09-05T12:48:29Z2020-12-14Mendes WS, Boechat CL, Gualberto AVS, Barbosa RS, Silva YJAB, Saraiva PC, Sena AFS, Duarte LSL. Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil. Rev Bras Cienc Solo. 2021;45:e0200115.1806-9657https://locus.ufv.br//handle/123456789/29841Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000–2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00–0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC.engRevista Brasileira de Ciência do SoloVol. 45, 2021.Creative Commons Attribution Licenseinfo:eu-repo/semantics/openAccesssoil spectroscopysoil predictionsshortwave infraredNIRSoil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdfTexto completoapplication/pdf2428381https://locus.ufv.br//bitstream/123456789/29841/1/artigo.pdf675a5b253f3d4caa0ffbbe5ccabe8dacMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/29841/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/298412022-09-05 09:48:29.578oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452022-09-05T12:48:29LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false |
dc.title.en.fl_str_mv |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
title |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
spellingShingle |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil Mendes, Wanderson de Sousa soil spectroscopy soil predictions shortwave infrared NIR |
title_short |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
title_full |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
title_fullStr |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
title_full_unstemmed |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
title_sort |
Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil |
author |
Mendes, Wanderson de Sousa |
author_facet |
Mendes, Wanderson de Sousa Boechat, Cácio Luiz Gualberto, Adriano Venicius Santana Barbosa, Ronny Sobreira Silva, Yuri Jacques Agra Bezerra da Saraiva, Paloma Cunha Sena, Antonny Francisco Sampaio de Duarte, Lizandra de Sousa Luz |
author_role |
author |
author2 |
Boechat, Cácio Luiz Gualberto, Adriano Venicius Santana Barbosa, Ronny Sobreira Silva, Yuri Jacques Agra Bezerra da Saraiva, Paloma Cunha Sena, Antonny Francisco Sampaio de Duarte, Lizandra de Sousa Luz |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Mendes, Wanderson de Sousa Boechat, Cácio Luiz Gualberto, Adriano Venicius Santana Barbosa, Ronny Sobreira Silva, Yuri Jacques Agra Bezerra da Saraiva, Paloma Cunha Sena, Antonny Francisco Sampaio de Duarte, Lizandra de Sousa Luz |
dc.subject.eng.fl_str_mv |
soil spectroscopy soil predictions shortwave infrared NIR |
topic |
soil spectroscopy soil predictions shortwave infrared NIR |
description |
Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000–2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00–0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-12-14 |
dc.date.accessioned.fl_str_mv |
2022-09-05T12:48:29Z |
dc.date.available.fl_str_mv |
2022-09-05T12:48:29Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Mendes WS, Boechat CL, Gualberto AVS, Barbosa RS, Silva YJAB, Saraiva PC, Sena AFS, Duarte LSL. Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil. Rev Bras Cienc Solo. 2021;45:e0200115. |
dc.identifier.uri.fl_str_mv |
https://locus.ufv.br//handle/123456789/29841 |
dc.identifier.issn.none.fl_str_mv |
1806-9657 |
identifier_str_mv |
Mendes WS, Boechat CL, Gualberto AVS, Barbosa RS, Silva YJAB, Saraiva PC, Sena AFS, Duarte LSL. Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil. Rev Bras Cienc Solo. 2021;45:e0200115. 1806-9657 |
url |
https://locus.ufv.br//handle/123456789/29841 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.pt-BR.fl_str_mv |
Vol. 45, 2021. |
dc.rights.driver.fl_str_mv |
Creative Commons Attribution License info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Creative Commons Attribution License |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Revista Brasileira de Ciência do Solo |
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Revista Brasileira de Ciência do Solo |
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reponame:LOCUS Repositório Institucional da UFV instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
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LOCUS Repositório Institucional da UFV |
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