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

• Published in: Journal of ICT Research and Applications Vol. 11; no. 1; pp. 78 - 91
• Main Authors: Krisnawijaya, N.N. Kutha, Herdiyeni, Yeni, Silalahi, Bib Paruhum
• Format: Journal Article
• Language: English
• Published: ITB Journal Publisher 01-01-2017
• Subjects: fuzzy local binary pattern; high performance computing; image processing; MPI Library; parallel processing; plant identification
• ISSN: 2337-5787; 2338-5499
• DOI: 10.5614/itbj.ict.res.appl.2017.11.1.5

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%.