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Rice Grain Classification using Fourier Transform and Morphological Features


  • Department of Computer Science, Christ University, Bengaluru – 560029, Karnataka, India


Background: Rice (Oryzasativa) is one of the most widely consumed staple foods, especially in the Asian subcontinent. Traditionally, grain testers who make use of calipers and other specialized tools for measuring features of grains are employed for classifying rice grains into different varieties. However, besides size and shape, various other characteristics, such as whiteness and chalkiness, also contribute towards its taste and overall quality. Objective: Computerized classification of rice grains can help in reducing errors of manual grading and in considering more features that indicate quality. The proposed work considers few commercially available rice grains in the South-Indian region to identify new attributes for better grain classification. Methodology: Rice samples were collected from rice outlets across Karnataka. Images of the grains were captured using flat-bed scanning technique and processed to extract new features that represented chalkiness and whiteness of grains along with other morphological features such as area, perimeter, major and minor axes, etc. Machine learning algorithms were used to create classification rule. Findings: The new features extracted were found to contribute significantly towards the classification. Nine varieties of rice grains were considered for the study and the system was able to successfully classify the grains with an accuracy of 95.78% using the NB Tree and SMO classifiers. Novelty: Many studies that consider the morphological features of grains such as its area, shape etc. have already been performed. However, the shapes and sizes of the different varieties are too varied to generalize a common formula for the classification of all varieties of rice. In this paper, Fourier features are also extracted from grain images in addition to the spatial features to arrive at an improved accuracy for classification.


Fast Fourier Transform (FFT), Grain Classification, Naive Bayes Tree (NB Tree), Rice (Oryzasativa), SMO (Sequential Minimal Optimization)

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  • Juliano BO. Rice in human nutrition. IRRI, FAO; Chapter 01, Introduction; Rome, Italy. 1993. p. 01–3.
  • Abirami S, Neelamegam P, Kala H. Analysis of rice granules using image processing and neural network pattern recognition tool. IJCA. 2014 Jun; 96(7):20–4. Crossref
  • Mahale B, Korde S. Rice quality analysis using image processing techniques. Convergence of Technology (I2CT).International Conference for Convergence of Technology 2014; IEEE; Pune. 2014 Apr. p. 1–5. Crossref
  • Mahajan S, Kaur S. Quality analysis of Indian basmati rice grains using top-hat transformation. IJCA. 2014 May; 94(15):42–8. Crossref
  • Tanck P, Kaushal B. A new technique of quality analysis for rice grading for Agmark standards. IJITEE. 2014 May; 3(12):83–5.
  • Veena H, Latharani TR. An efficient method for classification of rice grains using morphological process. IJIRAE.2014 Apr; 1(1):218–21.
  • Maheshwari CV. Quality assesment of Oryzasativa SSP Indica (rice) using computer vision. International Journal of Innovative Research in Computer and Communication Engineering.2013 Jun; 1(4):1107–15.
  • Gao H, Wang Y, Zhang G, Ge P, Liang Y. Rice kernel shape description using an improved Fourier descriptor. International Conference on Computer and Computing Technologies in Agriculture; Li D., Chen Y. editors. 2011; Beijing.China. Berlin Heidelberg: Springer. 2011. p. 104–14.
  • Kaur H, Singh B. Classification and grading rice using multi-class SVM. IJSRP. 2013 Apr; 3(4):1–5.
  • Desai D, Gamit N, Kinjal M. Grading of rice grains quality using image processing. IJMTER. 2015 Jul; 2(7):395–400.
  • Aulakh JS, Banga VK. Grading of rice grains by image processing.IJERT. 2012 Jun; 1(4):1–4.
  • Khunkhett S, Remsungnen T. Non-destructive identification of pure breeding rice seed using digital image analysis.The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE). IEEE; 2014 Mar 5-8. p. 1–4. Crossref
  • Silva CS, Sonnadara U. Classification of rice grains using neural networks. Proceedings of Technical Sessions; Sri Lanka, Institute of Physics. 2013. p. 9–14.
  • Kambo R, Yerpude A. Classification of basmati rice grain variety using image processing and principal component analysis. IJAREEIE. 2013 Jul; 2(7):80–5.
  • Patil V, Malemath VS. Quality analysis and grading of rice grain images. IJIRCCE. 2015 Jun; 3(6):5672–8.
  • Ajaz RH, Hussain L. Seed classification using machine learning techniques. JMEST. 2015 May; 2(5):1098–102.


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