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Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 1 (2017) · pp. 77–90

DOI
10.5614/itbj.ict.res.appl.2017.11.1.5
ISSN
2338-5499, 2337-5787
Published
2017-04-30
Publisher
DRI - ITB
Language
eng

Abstract

As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.

Keywords

Collaboration network of the authors

7 authors · 9 co-authorship links · 4 across different organisations

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  • IPB University
  • University of National Education
  • Politeknik Pertanian Negeri Payakumbuh
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dhea Fajriati AnasIPB University1
Indra JayaIPB University1
Indra LaksmanaPoliteknik Pertanian Negeri Payakumbuh1
Ervizal A.M. ZuhudIPB University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern</dc:title>
	<dc:creator>Kutha Krisnawijaya, Ngakan Nyoman</dc:creator>
	<dc:creator>Herdiyeni, Yeni</dc:creator>
	<dc:creator>Silalahi, Bib Paruhum</dc:creator>
	<dc:subject xml:lang="en-US">fuzzy local binary pattern</dc:subject>
	<dc:subject xml:lang="en-US">high performance computing</dc:subject>
	<dc:subject xml:lang="en-US">parallel processing</dc:subject>
	<dc:subject xml:lang="en-US">MPI Library</dc:subject>
	<dc:subject xml:lang="en-US">image processing</dc:subject>
	<dc:subject xml:lang="en-US">plant identification.</dc:subject>
	<dc:description xml:lang="en-US">As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-04-30</dc:date>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/2130</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.1.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 1 (2017); 77-90</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
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