Neural Network Application on Foliage Plant Identification

TL;DR

This paper presents a foliage plant identification system combining shape, color, texture, and vein features, achieving over 93% accuracy using a Probabilistic Neural Network classifier.

Contribution

It introduces a multi-feature approach including color, shape, texture, and vein features for improved foliage plant identification.

Findings

Achieved 93.08% average accuracy on 60 plant species.

Combined multiple features for better differentiation of similar leaf patterns.

Demonstrated effectiveness of PNN classifier in plant identification.

Abstract

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.