Welcome to the project:
Identification and phenotypic plasticity of vegetal tissues using complex systems
Abstract
Setting: The study of vegetal tissue microstructure is very important, because it can be used to analyze morphological characteristics, which are often related with phenotypic plasticity. The phenotypic plasticity of plants are morphological changes that occur in relation to the environment in which they are exposed. Therefore, in addition to obtain a better understanding about the plants, the study of plasticity allows a number of applications, for example: nutritional deficiency analysis.
Gap: Although there are very interesting works that study similar issues, especially in the area of the food engineering, there are only a few studies that have analyzed the plasticity of plants from a computational point of view.
Purpose: Given the demonstrated potential of complex systems techniques in pattern recognition tasks. This project aims to create, improve and verify which complex systems methods are more sensitive to phenotypic plasticity in microscopic images of vegetal tissues.
Materials and Methods: The methods we are planning to use vary from collecting microscopic images of plant leaves, to the application of image processing techniques to remove noise. Then, feature extraction based on complex system (fractals, cellular automata and complex networks) will be applied. Also, analysis through supervised and unsupervised learning techniques are going to be made. Finally, the results of the analysis will be compared and correlated with the plant phenotypic plasticity.
Results: There are not results yet.
Conclusions: There are not conclusions yet.
Gap: Although there are very interesting works that study similar issues, especially in the area of the food engineering, there are only a few studies that have analyzed the plasticity of plants from a computational point of view.
Purpose: Given the demonstrated potential of complex systems techniques in pattern recognition tasks. This project aims to create, improve and verify which complex systems methods are more sensitive to phenotypic plasticity in microscopic images of vegetal tissues.
Materials and Methods: The methods we are planning to use vary from collecting microscopic images of plant leaves, to the application of image processing techniques to remove noise. Then, feature extraction based on complex system (fractals, cellular automata and complex networks) will be applied. Also, analysis through supervised and unsupervised learning techniques are going to be made. Finally, the results of the analysis will be compared and correlated with the plant phenotypic plasticity.
Results: There are not results yet.
Conclusions: There are not conclusions yet.