  Several researches in leaf identification did not include color information
as features. The main reason is caused by a fact that they used green colored
leaves as samples. However, for foliage plants, plants with colorful leaves,
fancy patterns in their leaves, and interesting plants with unique shape, color
and also texture could not be neglected. For example, Epipremnum pinnatum
'Aureum' and Epipremnum pinnatum 'Marble Queen' have similar patterns, same
shape, but different colors. Combination of shape, color, texture features, and
other attribute contained on the leaf is very useful in leaf identification. In
this research, Polar Fourier Transform and three kinds of geometric features
were used to represent shape features, color moments that consist of mean,
standard deviation, skewness were used to represent color features, texture
features are extracted from GLCMs, and vein features were added to improve
performance of the identification system. The identification system uses
Probabilistic Neural Network (PNN) as a classifier. The result shows that the
system gives average accuracy of 93.0833% for 60 kinds of foliage plants.
