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dc.contributor.authorLOURENÇONI, D.
dc.contributor.authorSALVIANO, A. M.
dc.contributor.authorOLSZEVSKI, N.
dc.contributor.authorPEREIRA, J. S.
dc.contributor.authorCAVALCANTE, E. H. M.
dc.date.accessioned2026-03-09T11:18:30Z-
dc.date.available2026-03-09T11:18:30Z-
dc.date.created2026-03-09
dc.date.issued2026
dc.identifier.citationEarth Science Informatics, v. 19, n. 3, 28, Mar. 2026.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185169-
dc.descriptionSoil compaction, often the result of intensive mechanization, is a major concern because it increases the bulk density of the soil, fundamentally altering its structure and reducing its capacity to support essential ecological and agricultural functions. Therefore, soil monitoring is essential, particularly through its main quality indicator, bulk density. However, the analysis of this attribute requires the collection of soil samples with subsequent laboratory analysis, which demands time and qualified labor. Thus, this study aimed to develop a neuro-fuzzy model to estimate the bulk density of sandy soils based on variations in the proportion of its particle size fractions. To achieve this, undisturbed samples were collected from soils in four municipalities in northern Bahia, located along the shores of Lake Sobradinho. The proportion of particle size fractions in the composition of each sample was used to develop models that integrate fuzzy logic and neural networks. They employed the Takagi-Sugeno inference method and hybrid learning algorithms for parameter adjustment and bulk density predictions. The performance of the models was evaluated using error statistics, with the model with the highest accuracy in density predictions being selected, achieving an R² of 71%. It is important to emphasize that the neuro-fuzzy model did not require data stratification and can be applied to estimate the sandy soil density of any soil type sampled.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEstimation of bulk density
dc.subjectNeuro-fuzzy model
dc.subjectDensidade aparente
dc.subjectModelo neuro-fuzzy
dc.titleAdaptive neuro-fuzzy inference system for predicting sandy soils bulk density.
dc.typeArtigo de periódico
dc.subject.thesagroSolo Arenoso
dc.subject.nalthesaurusBulk density
dc.subject.nalthesaurusSandy soils
riaa.ainfo.id1185169
riaa.ainfo.lastupdate2026-03-09
dc.identifier.doihttps://doi.org/10.1007/s12145-026-02077-y
dc.contributor.institutionDIAN LOURENÇONI, UNIVERSIDADE FEDERAL DO VALE DO SÃO FRANCISCO; ALESSANDRA MONTEIRO SALVIANO, CNPS; NELCI OLSZEVSKI, UNIVERSIDADE FEDERAL DO VALE DO SÃO FRANCISCO; JANIELLE SOUZA PEREIRA, UNIVERSIDADE FEDERAL DE LAVRAS; EDMO HENRIQUE MARTINS CAVALCANTE, UNIVERSIDADE FEDERAL DO VALE DO SÃO FRANCISCO.
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