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https://rigeo.sgb.gov.br/handle/doc/24602
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Campo DC | Valor | Idioma |
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dc.contributor.author | ANDRADE, Renata | - |
dc.contributor.author | SILVA, Sérgio Henrique Godinho | - |
dc.contributor.author | BENEDET, Lucas | - |
dc.contributor.author | MANCINI, Marcelo | - |
dc.contributor.author | LIMA, Geraldo Jânio | - |
dc.contributor.author | NASCIMENTO, Kauan | - |
dc.contributor.author | AMARAL, Francisco Hélcio Canuto | - |
dc.contributor.author | SILVA, Douglas Ramos Guelfi | - |
dc.contributor.author | OTTONI, Marta Vasconcelos | - |
dc.contributor.author | CARNEIRO, Marco Aurélio Carbone | - |
dc.contributor.author | CURI, Nilton | - |
dc.date.accessioned | 2023-12-27T21:08:55Z | - |
dc.date.available | 2023-12-27T21:08:55Z | - |
dc.date.issued | 2023-07-26 | - |
dc.identifier.citation | ANDRADE, R.; SILVA, S. H. G.; BENEDET, L.; MANCINI, M.; LIMA, G. J.; NASCIMENTO, K.; AMARAL, F. H. C.; SILVA, D. R. G.; OTTONI, M. V.; CARNEIRO, M. A. C.; CURI, N. Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers. Environmental Research, Amsterdam, v. 236, 2023. DOI: https://doi.org/10.1016/j.envres.2023.116753. | pt_BR |
dc.identifier.issn | 0013-9351 | - |
dc.identifier.uri | https://rigeo.sgb.gov.br/handle/doc/24602 | - |
dc.description.abstract | Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fer tilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint. | pt_BR |
dc.language.iso | en | pt_BR |
dc.publisher | Elsevier | pt_BR |
dc.rights | open | pt_BR |
dc.title | Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers | pt_BR |
dc.type | Article | pt_BR |
dc.local | Amsterdam | pt_BR |
dc.creator.affilliation | Universidade Federal de Lavras - Minas Gerais | pt_BR |
dc.creator.affilliation | Companhia de Promoção de Agricultura - CAMPO - Paracatu - Minas Gerais | pt_BR |
dc.creator.affilliation | Eldorado Brasil - Três Lagoas - Mato Grosso do Sul | pt_BR |
dc.creator.affilliation | Análises Agrícolas e Pecuárias - 3º Laboratório - Lavras - Minas Gerais | pt_BR |
dc.creator.affilliation | Serviço Geológico do Brasil - CPRM | pt_BR |
dc.description.embargo | 2023-12-27 | - |
dc.subject.en | pXRF | pt_BR |
dc.subject.en | Machine learning | pt_BR |
dc.subject.en | Green analysis | pt_BR |
dc.subject.en | Soil health | pt_BR |
dc.subject.en | Soil contamination | pt_BR |
dc.subject.en | Macronutrients | pt_BR |
dc.subject.en | Micronutrients | pt_BR |
Aparece nas coleções: | Artigos de Periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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Andrade et al., 2023 Fertilizers.pdf | Artigo de periódico | 9,66 MB | Adobe PDF | Visualizar/Abrir |
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