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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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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
score 13.222552

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