A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features

TL;DR

This paper presents a novel plant identification system combining GLCM, lacunarity, and Shen features with a Bayesian classifier, achieving high accuracy and outperforming existing methods on standard datasets.

Contribution

The study introduces a new approach integrating multiple texture and shape features with Bayesian classification for improved plant identification accuracy.

Findings

Achieved 97.19% accuracy on Flavia dataset.

Achieved 95.00% accuracy on Foliage dataset.

Outperforms existing plant identification methods.

Abstract

Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.