Convolutional Neural Network approach for different leaf classification

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Main Authors:	Ahmad Nazri, Ali, Lee, Shin En
Other Authors:	nazriali@usm.my
Format:	Article
Language:	English
Published:	Institute of Engineering Mathematics, Universiti Malaysia Perlis 2023
Subjects:	Convolutional Neural Networks Image processing Machine learning
Online Access:	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77665
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-77665
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spelling	my.unimap-776652023-01-12T04:15:37Z Convolutional Neural Network approach for different leaf classification Ahmad Nazri, Ali Lee, Shin En Ahmad Nazri, Ali nazriali@usm.my School of Electrical and Electronic Engineering, University Science Malaysia (USM), Engineering Campus Convolutional Neural Networks Image processing Machine learning Link to publisher's homepage at https://amci.unimap.edu.my/ There are millions of plant species with different shapes of a leaf. Those unfamiliar or outside the field may have difficulty recognizing the plant based on leaf appearances. A system that can provide an automatic response when a kind of leaf is exhibited may need to be developed. The system should provide the name of the leaf and other related information according to the input image. Therefore, in this paper, a research work on developing a system that can classify the leaf types is performed. The Convolutional Neural Network (CNN) architecture is applied with the help of TensorFlow for modeling the training data and testing. The classification accuracies are evaluated and tested on the leaf datasets where the unknown leaf image is used as input, and the name of the plant species belonging to the input image is classified as the system's output. The assessment showsthat the trained model can achieve a performance accuracy of more than 95%, which provides a promising system for the public to classify leaves and understand nature much more deeply 2023-01-12T04:15:37Z 2023-01-12T04:15:37Z 2022-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 386-398 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77665 https://amci.unimap.edu.my/ en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Convolutional Neural Networks Image processing Machine learning
spellingShingle	Convolutional Neural Networks Image processing Machine learning Ahmad Nazri, Ali Lee, Shin En Ahmad Nazri, Ali Convolutional Neural Network approach for different leaf classification
description	Link to publisher's homepage at https://amci.unimap.edu.my/
author2	nazriali@usm.my
author_facet	nazriali@usm.my Ahmad Nazri, Ali Lee, Shin En Ahmad Nazri, Ali
format	Article
author	Ahmad Nazri, Ali Lee, Shin En Ahmad Nazri, Ali
author_sort	Ahmad Nazri, Ali
title	Convolutional Neural Network approach for different leaf classification
title_short	Convolutional Neural Network approach for different leaf classification
title_full	Convolutional Neural Network approach for different leaf classification
title_fullStr	Convolutional Neural Network approach for different leaf classification
title_full_unstemmed	Convolutional Neural Network approach for different leaf classification
title_sort	convolutional neural network approach for different leaf classification
publisher	Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate	2023
url	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77665
_version_	1772813095438123008

Convolutional Neural Network approach for different leaf classification

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