Leaf Classification Using Shape, Color, and Texture Features

TL;DR

This paper presents a leaf classification method that combines shape, color, and texture features using a Probabilistic Neural Network, achieving high accuracy on the Flavia dataset and improving upon previous methods.

Contribution

The study introduces a comprehensive feature set including color, shape, and texture for leaf classification, utilizing PNN for improved accuracy.

Findings

Achieved 93.75% accuracy on Flavia dataset

Incorporating color features improves classification performance

Outperforms previous leaf classification methods

Abstract

Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In this case, a neural network called Probabilistic Neural network (PNN) was used as a classifier. The experimental result shows that the method for classification gives average accuracy of 93.75% when it was tested on Flavia dataset, that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work.