We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Geometric Feature Extraction of Selected Rice Grains using Image Processing Techniques

by Sukhvir Kaur, Derminder Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 8
Year of Publication: 2015
Authors: Sukhvir Kaur, Derminder Singh
10.5120/ijca2015905576

Sukhvir Kaur, Derminder Singh . Geometric Feature Extraction of Selected Rice Grains using Image Processing Techniques. International Journal of Computer Applications. 124, 8 ( August 2015), 41-46. DOI=10.5120/ijca2015905576

@article{ 10.5120/ijca2015905576,
author = { Sukhvir Kaur, Derminder Singh },
title = { Geometric Feature Extraction of Selected Rice Grains using Image Processing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 8 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number8/22127-2015905576/ },
doi = { 10.5120/ijca2015905576 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:53.550454+05:30
%A Sukhvir Kaur
%A Derminder Singh
%T Geometric Feature Extraction of Selected Rice Grains using Image Processing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 8
%P 41-46
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rice grains quality estimation is important in fulfilling customer requirements. Geometric features of grains are used to check the quality of rice grains. Mechanical classification methods are being used largely by local industry to grade different size of food grains on basis of geometric parameters. Image processing techniques can be applied to extract various features of rice grains and classifies the grains based on geometric features. This study proposed a method that processes the captured still digital image of rice grains. The program has been developed using MATLAB technology. The compiler of this technology was used to convert the program into standalone application.  Application was embedded with MATLAB compiler runtime that enables the execution of compiled application on computers that do not have this technology installed. In this method seven geometric features of individual rice grain were extracted from digital images and then grains of particular varieties were classified into three different classes. Calibration factor was calculated to make the method independent of camera position. The method was tested on five varieties of rice grains and compared to experimental results by measuring the geometric features of rice grains using digital vernier caliper. The error rate of measuring different geometric features between proposed method and experimental analysis was found between -1.39% and 1.40%.

References
  1. Brosnan T and Sun D W (2002) Inspection and grading of agricultural and food products by computer vision systems – a review. Computers and Electronics in Agriculture 36:193-213.
  2. Camelo GA (2012) et al Digital image analysis of diverse Mexican rice cultivars .Journal of the Science of Food and Agriculture 92:2709-2714.
  3. Gujjar H S and Siddappa M (2013) A Method for identification of basmati rice grain of India and its quality using pattern classification. International Journal of Engineering Research and Applications 3:268-273.
  4. Guzman J D and Peralta E K (2008) Classification of Philippine rice grains using machine vision and artificial neural networks. World Conference on Agricultural Information and IT 19:41-48.
  5. Kaur G, Din S, Brar A S and Singh D (2014) Scanner image analysis to estimate leaf area. International Journal of Computer Applications 107:5-10.
  6. Kaur H, Singh B (2013) Classification and grading rice using multi-class svm. International Journal of Scientific and Research Publications 3:1-5.
  7. Liu Z Y, Cheng F J, Ying Y B and Rao X Q (2005) Identification of rice seed varieties using neural network. Journal of Zhejiang University Science 11: 1095-1100.
  8. Maheshwari C V, Jain K R and Modi C K (2012) Non-destructive quality analysis of Indian Gujrat-17 oryza sativa ssp indica (rice) using image processing. International Journal of Computer Engineering Science 2:48-54.
  9. Patil N K, Malemath V S and Yadahalli R M (2011) Color and texture based identification and classification of food grains using different color models and haralick features. International Journal on Computer Science and Engineering 3:3669-3680.
  10. Shantaiya S and Ansari U (2010) Identification of food grains and its quality using pattern classification. International Journal of Computer & Communication Technology 2:70-74.
  11. Singh T, Kumar CM, Singh P and Kumar P (2013) Advances in computer vision technology for foods of animal and aquatic origin- a review. Journal of Meat Science and Technology 1:40-49.
Index Terms

Computer Science
Information Sciences

Keywords

Rice grains Classification Feature Extraction Image Processing