Biometric analysis of leaf venation density based on Digital Image

Abstract

The density level in the leaf venation type has different characteristics. These different characteristics explain the environment in which plants grow, such as habitat, vegetation, physiology and climate. This research aims to measure of leaf venation density, leaf venation feature analysis and then identifying plants based on venation type. Stages of this research include leaf image data collection, segmentation, vein detection, feature extraction, feature selection, classification, evaluation and ending with analysis. The results of this study indicate that the level of leaf venation density is quite good is the type of venation paralellodromous, acrodromous and pinnate. Based on the selection of features using Boruta Algorithm, obtained 19 most important features that represent the type of leaf venation. This is reinforced by the average of accuracy produced at the time of classification using SVM, which amounted to 77.57%.