Detalhes bibliográficos
| Ano de defesa: |
2016 |
| Autor(a) principal: |
Morais, Pedro Augusto de Oliveira
 |
| Orientador(a): |
Oliveira, Anselmo Elcana de
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| Banca de defesa: |
Oliveira, Anselmo Elcana de,
Coelho, Clarimar José,
Antoniosi Filho, Nelson Roberto |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Goiás
|
| Programa de Pós-Graduação: |
Programa de Pós-graduação em Química (IQ)
|
| Departamento: |
Instituto de Química - IQ (RG)
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://repositorio.bc.ufg.br/tede/handle/tede/9883
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Resumo: |
The texture or grain size of the surface are de ned by the quantitative distribution of the mineral particles smaller than 2 mm: sand, clay and silt. These physical indicators enable soil classi cation and guide the management, irrigation and the addition of agricultural inputs. Although the usual methods for textural analysis are laborious and destructive, using chemical oxidizing agents, this kind of analysis is quite required in soil fertility laboratories. Therefore, it is essential to research and develop alternative methodologies that are operational and clean. In this way, this study proposes the use of multivariate analysis of digital images to predict and classify soil texture. For this purpose, 60 samples of diverse soil were considered to textural analysis by the pipette method and for obtaining digital images in color system RGB (Red, Green, Blue) in Ti format. The correlation between digital images and the percentage of sand, clay and silt is made by Partial Least Squares Regression (PLS) and Multiple Linear Regression algorithm associated with the Successive Projections (SPA-MLR). The best models had a 100 % success rate. Therefore, the prediction texture soil through images is a promising technique to be clean, inexpensive and operational. |
