Consulta: subjectFacets:"Prediction models"
Registros recuperados: 3
Data/hora: 13/06/2024 05:25:51

Digital mapping of soil attributes using machine learning
Provedor de dados: Revista Ciência Agronômica (RCA)
Autores: Campbell,Patrícia Morais da Matta; Francelino,Márcio Rocha; Fernandes Filho,Elpídio Inácio; Rocha,Pablo de Azevedo; Azevedo,Bruno Campbell de.
ABSTRACT Mapping the chemical attributes of the soil on a large scale can result in gains when planning the use and occupation of the land. There are different techniques available for this purpose, whose performance should be tested for different types of landscapes. The aim of this study was to spatialize chemical attributes of the soil, comparing eight methods of prediction. Forty morphometric attributes, generated from a digital elevation model, were used as independent variables, in addition to geophysical data, images from the Landsat 8 satellite and the NDVI. All possible combinations between the satellite bands were calculated, generating 28 new variables. Combinations between the Th, U and K bands obtained from the geophysical data were also...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: XRF; Spatial approach; Prediction models.
Ano: 2019
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902019000400519

Neural network and state-space models for studying relationships among soil properties
Provedor de dados: Scientia Agricola
Autores: Timm,Luís Carlos; Gomes,Daniel Takata; Barbosa,Emanuel Pimentel; Reichardt,Klaus; Souza,Manoel Dornelas de; Dynia,José Flávio.
The study of soil property relationships is of great importance in agronomy aiming for a rational management of environmental resources and an improvement of agricultural productivity. Studies of this kind are traditionally performed using static regression models, which do not take into account the involved spatial structure. This work has the objective of evaluating the relation between a time-consuming and "expensive" variable (like soil total nitrogen) and other simple, easier to measure variables (as for instance, soil organic carbon, pH, etc.). Two important classes of models (linear state-space and neural networks) are used for prediction and compared with standard uni- and multivariate regression models, used as reference. For an oat crop...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Soil attributes; Prediction models; Spatial transect; Latent variables.
Ano: 2006
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162006000400010

Correcting field determination of elemental contents in soils via portable X-ray fluorescence spectrometry
Provedor de dados: Ciência e Agrotecnologia
Autores: Dijair,Thaís Santos Branco; Silva,Fernanda Magno; Teixeira,Anita Fernanda dos Santos; Silva,Sérgio Henrique Godinho; Guilherme,Luiz Roberto Guimarães; Curi,Nilton.
ABSTRACT Portable X-ray fluorescence (pXRF) spectrometry has been useful worldwide for determining soil elemental content under both field and laboratory conditions. However, the field results are influenced by several factors, including soil moisture (M), soil texture (T) and soil organic matter (SOM). Thus, the objective of this work was to create linear mathematical models for conversion of Al2O3, CaO, Fe, K2O, SiO2, V, Ti and Zr contents obtained by pXRF directly in field to those obtained under laboratory conditions, i.e., in air-dried fine earth (ADFE), using M, T and SOM as auxiliary variables, since they influence pXRF results. pXRF analyses in field were performed on 12 soil profiles with different parent materials. From them, 59 samples were...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: PXRF; Soil moisture; Soil texture; Soil organic matter; Prediction models.
Ano: 2020
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542020000100211