PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES
Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficient...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Slovenian Society for Stereology and Quantitative Image Analysis
2018-07-01
|
Series: | Image Analysis and Stereology |
Subjects: | |
Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/1821 |
id |
doaj-f5c8363f284249bbbaeeb2361aa1bd02 |
---|---|
record_format |
Article |
spelling |
doaj-f5c8363f284249bbbaeeb2361aa1bd022020-11-25T00:06:26ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652018-07-0137211912610.5566/ias.1821999PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVESJules R Kala0Serestina Viriri1University of KwaZulu-NatalUniversity of KwaZulu-Natal, School of Maths, Statistics & Computer ScienceForests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.https://www.ias-iss.org/ojs/IAS/article/view/1821leaf recognitionplant classificationsinuosity coefficientssinuosity measure |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jules R Kala Serestina Viriri |
spellingShingle |
Jules R Kala Serestina Viriri PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES Image Analysis and Stereology leaf recognition plant classification sinuosity coefficients sinuosity measure |
author_facet |
Jules R Kala Serestina Viriri |
author_sort |
Jules R Kala |
title |
PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES |
title_short |
PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES |
title_full |
PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES |
title_fullStr |
PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES |
title_full_unstemmed |
PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES |
title_sort |
plant specie classification using sinuosity coefficients of leaves |
publisher |
Slovenian Society for Stereology and Quantitative Image Analysis |
series |
Image Analysis and Stereology |
issn |
1580-3139 1854-5165 |
publishDate |
2018-07-01 |
description |
Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition. |
topic |
leaf recognition plant classification sinuosity coefficients sinuosity measure |
url |
https://www.ias-iss.org/ojs/IAS/article/view/1821 |
work_keys_str_mv |
AT julesrkala plantspecieclassificationusingsinuositycoefficientsofleaves AT serestinaviriri plantspecieclassificationusingsinuositycoefficientsofleaves |
_version_ |
1725422098636079104 |