Application of image analysis for the varietal classification of barley:. Morphological features

Journal of Cereal Science

Volumn 48, Issue 1, 2008, Pages 104-110

  Zapotoczny, Piotr ^a   Zielinska, Magdalena ^a   Nita, Zygmunt ^b  

^a UNIVERSITY OF WARMIA AND MAZURY IN OLSZTYN   (Poland)

^b Research Farm in Strzelce   (Poland)

Author keywords

Barley; Cereal grain classification; Digital image analysis; Morphological features

Indexed keywords

HORDEUM;

EID: 43049175162     PISSN: 07335210     EISSN: 10959963     Source Type: Journal    
DOI: 10.1016/j.jcs.2007.08.006     Document Type: Article

References (19)

1. Barker D.A., Vouri T.A., Hegedus M.R., and Myers D.G. The use of ray parameters for the discrimination of Australian wheat varieties. Plant Varieties and Seeds 5 (1992) 35-45
2. Barker D.A., Vouri T.A., and Myers D.G. The use of slice and aspect ratio parameters for the discrimination of Australian wheat varieties. Plant Varieties and Seeds 5 (1992) 47-52
3. Barker D.A., Vouri T.A., and Myers D.G. The use of Fourier descriptors for the discrimination of Australian wheat varieties. Plant Varieties and Seeds 5 (1992) 93-102
4. Barker D.A., Vouri T.A., Hegedus M.R., and Myers D.G. The use of Chebychev coefficients for the discrimination of Australian wheat varieties. Plant Varieties and Seeds 5 (1992) 103-111
5. Cober E.R., Voldeng H.D., and Fregeau-Reid J.A. Heritability of seed shape and seed size in soybean. Crop Science 37 (1997) 1767-1769
6. Jayas D.S., Paliwal J., and Visen N.S. Multi-layer neural networks for image analysis of agricultural products. Journal of Agricultural Engineering Research 77 (2000) 119-128
7. Majumdar S., and Jayas D.S. Classification of cereal grains using machine vision: I. Morphology models. Transactions of the ASAE 43 (2000) 1669-1675
8. Myers D.G., and Edsall K.J. The application of image processing techniques to the identification of Australian wheat varieties. Plant Varieties and Seeds 2 (1989) 109-116
9. Neuman M., Sapirstein H.D., Shwedyk E., and Bushuk W. Discrimination of wheat class and variety by digital image analysis of whole grain samples. Journal of Cereal Science 6 (1987) 125-132
10. Pavlidis T. Algorithms for shape analysis of contours and waveforms. IEEE Transactions Pattern Analysis and Machine Intelligence 2 (1980) 301-312
11. Sapirstein, H.D., Bushuk, W., 1989. Quantitative determination of foreign material and vitreosity in wheat by digital image analysis. In: Salovaara, H. (Ed.), Proceedings of the ICC'89 Symposium on Wheat End-Use Properties, Lahti, Finland, pp. 453-474.
12. Sapirstein H.D., and Kohler J.M. Effects of sampling and wheat grade on precision and accuracy of kernel features determined by digital image analysis. Cereal Chemistry 76 (1999) 110-115
13. Sapirstein H.D., Neuman M., Wright E.H., Shwedyk E., and Bushuk W. An instrumental system for cereal grain classification using digital image analysis. Journal of Cereal Science 6 (1987) 3-14
14. Shouche S.P., Rastogi R., Bhagwat S.G., and Sainis J.K. Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture 33 (2001) 55-76
15. Sun D.W., and Du C.J. Recent developments in the application of image processing techniques for food quality evaluation. Trends in Food Science and Technology 15 (2004) 230-249
16. Symons S.J., and Fulcher R.G. Determination of wheat kernel morphological variation by digital image analysis: I. Variation in Eastern Canadian milling quality wheats. Journal of Cereal Science 8 (1988) 211-218
17. Symons S.J., and Fulcher R.G. Determination of wheat kernel morphological variation by digital image analysis: I. Variation in cultivars of soft white winter wheats. Journal of Cereal Science 8 (1988) 219-229
18. Zayas I., Pomeranz Y., and Lai F.S. Discrimination between Arthur and Arkan wheats by image analysis. Cereal Chemistry 62 (1985) 478-480
19. Zayas I., Lai F.S., and Pomeranz Y. Discrimination between wheat classes and varieties by image analysis. Cereal Chemistry 63 (1986) 52-56