A Comparative Experiment of Several Shape Methods in Recognizing Plants

TL;DR

This study compares four shape-based methods for plant recognition, introducing Zernike moments and PFT, with PFT achieving the highest accuracy of 64% across 52 plant types.

Contribution

It presents a comparative analysis of shape methods for plant identification, including novel use of Zernike moments and PFT in this context.

Findings

PFT achieved the highest accuracy of 64%.

Zernike moments and PFT were used for the first time in plant identification.

Shape features are effective for recognizing diverse plant species.

Abstract

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.