Published on Fri Oct 07 2011

A Comparative Experiment of Several Shape Methods in Recognizing Plants

A. Kadir, L. E. Nugroho, A. Susanto, P. I. Santosa

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Two approaches have never been used in plants identification yet. Polar Fourier Transform (PFT) gave best performance with 64% in accuracy.

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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.

Tue Nov 20 2018
Computer Vision
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Sun Sep 13 2020
Artificial Intelligence
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Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein. As computers cannot comprehend images, they are required to be converted into features by individually analysing image shapes, colours, textures and moments.
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Plant species identification is time consuming, costly, and requires lots of expertise knowledge. We explore the use of interpretable, measurable and computer-aided features extracted from plant leaf images. We introduced 52 features to classify plant species. The results show that the features are sufficient to discriminate the classes of interest.
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Thu Apr 11 2013
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