Herb leaves pattern recognition using digital microscope and deep learning
Herb leaves are not fully exploited for their benefits due to the problem arise from difficulties in identifying the type of plants. Visual-based pattern recognition via microscopic imaging provides high precision and accuracy for identification that easily outperforms the limitations of human perce...
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Persatuan Saintis Muslim Malaysia (PERINTIS)
2019
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iium-783452020-03-09T02:59:00Z http://irep.iium.edu.my/78345/ Herb leaves pattern recognition using digital microscope and deep learning Rahman, Muhammad Ariff Azizul Gunawan, Teddy Surya Kartiwi, Mira TK7885 Computer engineering Herb leaves are not fully exploited for their benefits due to the problem arise from difficulties in identifying the type of plants. Visual-based pattern recognition via microscopic imaging provides high precision and accuracy for identification that easily outperforms the limitations of human perception. A three-class dataset of 255 leaf vein images - betel, noni and ortosiphon stamineus, were captured by a digital microscope was constructed as a training set for a visual-based pattern recognition system. A simple visual-based pattern recognition system was constructed by using MATLAB and deep learning. Transfer learning was applied on a pretrained VGG-16 network with a validation accuracy of 91.03%. Further fine-tuning was applied on the network resulting an increase of accuracy to 98.72%. Persatuan Saintis Muslim Malaysia (PERINTIS) 2019 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/78345/13/78345%20HERB%20LEAVES%20PATTERN.pdf Rahman, Muhammad Ariff Azizul and Gunawan, Teddy Surya and Kartiwi, Mira (2019) Herb leaves pattern recognition using digital microscope and deep learning. In: International Conference on Universal Wellbeing (ICUW2019), 4th-6th December 2019, Kuala Lumpur, Malaysia. https://drive.google.com/file/d/1SBEjdqK7UqMmqu0e5lKagPvCa1FgbVIT/view |
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language |
English |
topic |
TK7885 Computer engineering |
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TK7885 Computer engineering Rahman, Muhammad Ariff Azizul Gunawan, Teddy Surya Kartiwi, Mira Herb leaves pattern recognition using digital microscope and deep learning |
description |
Herb leaves are not fully exploited for their benefits due to the problem arise from difficulties in identifying the type of plants. Visual-based pattern recognition via microscopic imaging provides high precision and accuracy for identification that easily outperforms the limitations of human perception. A three-class dataset of 255
leaf vein images - betel, noni and ortosiphon stamineus, were captured by a digital microscope was constructed as a training set for a visual-based pattern recognition
system. A simple visual-based pattern recognition system was constructed by using MATLAB and deep learning. Transfer learning was applied on a pretrained VGG-16
network with a validation accuracy of 91.03%. Further fine-tuning was applied on the network resulting an increase of accuracy to 98.72%. |
format |
Conference or Workshop Item |
author |
Rahman, Muhammad Ariff Azizul Gunawan, Teddy Surya Kartiwi, Mira |
author_facet |
Rahman, Muhammad Ariff Azizul Gunawan, Teddy Surya Kartiwi, Mira |
author_sort |
Rahman, Muhammad Ariff Azizul |
title |
Herb leaves pattern recognition using digital microscope and deep learning |
title_short |
Herb leaves pattern recognition using digital microscope and deep learning |
title_full |
Herb leaves pattern recognition using digital microscope and deep learning |
title_fullStr |
Herb leaves pattern recognition using digital microscope and deep learning |
title_full_unstemmed |
Herb leaves pattern recognition using digital microscope and deep learning |
title_sort |
herb leaves pattern recognition using digital microscope and deep learning |
publisher |
Persatuan Saintis Muslim Malaysia (PERINTIS) |
publishDate |
2019 |
url |
http://irep.iium.edu.my/78345/ http://irep.iium.edu.my/78345/ http://irep.iium.edu.my/78345/13/78345%20HERB%20LEAVES%20PATTERN.pdf |
first_indexed |
2023-09-18T21:50:24Z |
last_indexed |
2023-09-18T21:50:24Z |
_version_ |
1777413730681749504 |