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Frequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
Home Archives Volume 5 Issue 4 ijcatr05041001

IJCATR Volume 5 Issue 4

Rice Seed Germination Analysis

Benjamaporn Lurstwut Chomtip Pornpanomchai
10.7753/IJCATR0504.1001
keywords : Rice seed, seed germination, rice seed features, image processing, computer vision

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This research aimed to develop the computer software called “Rice Seed Germination Analysis (RiSGA)” which could predict rice seed image for rice germination by using an image processing technique. The RiSGA consisted of five main process modules: 1) image acquisition, 2) image pre-processing, 3) feature extraction, 4) quality control analysis and 5) quality results. Six variations of Thai rice seed species (CP111, RD41, Chiang Phattalung, Sang Yod Phattalung, Phitsanulok 2 and Chai Nat 1) were used for the experiment. The RiSGA extracted three main features: 1) color, 2) morphological and 3) texture feature. The RiSGA applied four well-known techniques: 1) Euclidean Distance (ED), 2) Rule Based System (RBS), 3) Fuzzy Logic (FL) and 4) Artificial Neural Network (ANN). The RiSGA precision of ED, RBS, FL, and ANN was 87.50%, 100%, 100%, and 100%, respectively. The average access time was 4.35 seconds per image, 5.29 seconds per image, 7.04 seconds per image, and 159.65 seconds per image, respectively.
@artical{b542016ijcatr05041001,
Title = "Rice Seed Germination Analysis",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "5",
Issue ="4",
Pages ="176 - 182",
Year = "2016",
Authors ="Benjamaporn Lurstwut Chomtip Pornpanomchai"}
  • The paper proposes the rice seed germination analysis system called “RiSGA” that developed by using image processing.
  • The RiSGA system consisted of five processing modules which are image acquisition, image preprocessing, feature extraction, quality control analysis and quality results.
  • Six variation of Thai rice seed species (CP111, Chiang Phatthalung, Sang Yod Phattalung, Phitsanulok 2 and Chai Nat 1) were selected as the data set used in the germination experiment.
  • The RiSGA applied four well-known techniques which are Euclidean Distance, Rule Based System, Fuzzy Logic and Artificial Neural Network.
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