Vol. 8 (05) pp. 578-585 DOI: 10.21474/IJAR01/10963

DISCRIMINATION OF PADDY VARIETIES USING WAVELET FEATURES

14 Downloads 52 Views
Crossref

Download Full Paper (PDF)

Abstract

This research proposes an algorithm to implement feature extraction technique using wavelet, and use the extracted coefficients to represent the image for classification of Grains. A total of 75 Wavelet features were extracted from the high-resolution images of paddy grains. The wavelet features were employed along with ANN to identify paddy varieties. This research is aimed at comparing Single-level discrete 2-D wavelet transform and Multilevel 2-D wavelet decomposition, using ANN for discriminating Indian Paddy Varieties and also evaluate variety-wise classification of individual grains. An evaluation of the classification accuracy of wavelet features and ANN was done to classify four Paddy (Rice) grains, viz. Karjat-6(K6) and Ratnagiri-2(R2), Ratnagiri-4(R4) and Ratnagiri-24(R24). All feature models were tested for their ability to classify these cereal grains and the most suitable feature was identified from the Wavelet features for accurate classification. Single-level discrete 2-DWT gave the best classification using ANN and more accuracy can be obtained by increasing the levels of decomposition.

Keywords

Article Analytics

References

  1. Borah S; Hines E L, ?Bhuyan M Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules,? Journal of Food Engineering, 79, 2007, 629?639
  2. Duda R O; Hart P E , ?Pattern Classification and Scene Analysis,? John Wiley and Sons Inc., New York, 1973
  3. Gonzalez R C; Woods R E; Eddins S L, ?Digital Image Processing using MATLAB,? Pearson Education, Inc., Upper Saddle River, NJ, USA, 2004
  4. Jayas D S; Paliwal J; Visen N S, ?Multi-layer neural networks for image analysis of agricultural products,? Journal of Agricultural Engineering Research, 77(2), 2000, 119?128
  5. Jeyamkondan S. ?Nondestructive evaluation of beef palatability,? PhD Thesis, Oklahoma State University, Stillwater, OK, USA, 2004
  6. Majumdar S; Jayas D S; Symons S J, ?Textural features for grain identification,? Agricultural Engineering Journal, 8(4), 1999, 213?222
  7. Majumdar S; Jayas D S, ?Classification of cereal grains using machine vision. I. Morphology models,? Transactions of the ASAE, 43(6), 2000a, 1669?1675
  8. Majumdar S; Jayas D S, ?Classification of cereal grains using machine vision. II. Color models,? Transactions of the ASAE, 43(6), 2000b, 1677?1680
  9. Majumdar S; Jayas D S, ?Classification of cereal grains using machine vision. III. Texture models,? Transactions of the ASAE, 43(6), 2000c, 1681?1687
  10. Majumdar S; Jayas D S, ?Classification of cereal grains using machine vision. IV. Combined Morphology, color, and texture models,? Transactions of the ASAE, 43(6), 2000d, 1689?1694
  11. Mallat S G, ?A theory for multiresolution signal decomposition: the wavelet representation,? IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 1989. 674?693
  12. Mojsilovic A; Popovic M V; Rackov D M. ?On the selection of an optimal wavelet basis for texture characterization,? IEEE Transactions on Image Processing, 9(12), 2000,2043?2050
  13. Paliwal J; Shashidhar N S; Jayas D S, ?Grain kernel identification using kernel signature,? Transactions of the ASAE, 42(6), 1999, 1921?1924
  14. Paliwal J; Visen N S; Jayas D S, ?Evaluation of neural network architectures for cereal grain classification using morphological features,? Journal of Agricultural Engineering Research, 79(4), 2001, 361?370
  15. Choudhary, J. Paliwal, D.S. Jayas, ?Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images,? Biosystems engineering, 99, 2008, 330 ? 337
  16. Choudhary, S. Mahesh, J. Paliwal, D.S. Jayas, ?Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples,? Biosystems engineering, 102, 2009, 115?127
  17. Sarlashkar, A.N.;??Bodruzzaman, M.?;?Malkani, M.J., ?Feature extraction using wavelet transform for neural network based image classification,? System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on, 412 ? 416
  18. Walker J S, ?A Primer on Wavelets and their Scientific Applications,? Chapman & Hall/CRC, Boca Raton, FL, USA, 1999
  19. Zheng C; Sun DW; Zheng L, ?Classification of tenderness of large cooked beef joints using wavelet and gabor textural features,? Transactions of the ASABE, 49(5), 2006, 1447?1454.

How to Cite This Article

Archana Chaugule (2020); DISCRIMINATION OF PADDY VARIETIES USING WAVELET FEATURES, Int. J. of Adv. Res., 8 (05), 578-585, ISSN 2320-5407. DOI: https://doi.org/10.21474/IJAR01/10963

Corresponding Author

Dr. Archana Chaugule

Creative Commons Attribution 4.0 This work is licensed under a Creative Commons Attribution 4.0 International License.