Previsão e classificação textural do solo através da análise multivariada de imagens
Nenhuma Miniatura disponível
Data
2016-03-08
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
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.
Descrição
Palavras-chave
Citação
MORAIS, Pedro Augusto de Oliveira. Previsão e classificação textural do solo através da análise multivariada de imagens. 2016. 190 f. Dissertação (Mestrado em Química) - Universidade Federal de Goiás, Goiânia, 2016.