In this work we present a simulation of a recognition process with perimeter characterization of a simple
plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and
orientation may penalize performance recognition for some varieties. Border description sequences are then
used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study
which is the best number of components to be considered for the classification task, implemented by means
of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory,
and compared with some references our system improves the recognition success up to 80.8% depending on
the number of considered independent components
