Comparison of statistical and neural network methods for classifying cereal grains using machine vision

Transactions of the American Society of Agricultural Engineers

Volumn 42, Issue 2, 1999, Pages 413-419

  Luo, X ^a   Jayas, D S ^a   Symons, S J ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Automation; Digital image processing; Grain handling; Identification; Image processing

Indexed keywords

AUTOMATION; CLASSIFIERS; COMPUTER VISION; DIGITAL SIGNAL PROCESSING; EQUIPMENT TESTING; FEATURE EXTRACTION; GRAIN (AGRICULTURAL PRODUCT); IMAGE PROCESSING; MATERIALS HANDLING; NEURAL NETWORKS; STATISTICAL METHODS;

CEREAL GRAINS; CLASSIFICATION ACCURACY; GRAIN HANDLING; STATISTICAL CLASSIFIERS;

AGRICULTURAL MACHINERY;

EID: 0033390779     PISSN: 00012351     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. Barker, D. A., T. A. Vouri, M. R. Hegedus, and D. G. Myers. 1992a. The use of ray parameters for the discrimination of Australian wheat varieties. Plant Varieties & Seeds 5: 35-45.
2. Barker, D. A., T. A. Vouri, and D. G. Myers. 1992b. The use of slice and aspect ratio parameters for the discrimination of Australian wheat varieties. Plant Varieties & Seeds 5:47-52.
3. 1992c. The use of Fourier descriptors for the discrimination of Australian wheat varieties. Plant Varieties & Seeds 5: 93-102.
4. Barker, D. A., T. A. Vouri, M. R. Hegedus, and D. G. Myers. 1992d. The use of Chebychev coefficients for the discrimination of Australian wheat varieties. Plant Varieties & Seeds 5: 103-111.
5. Brandon, J. R., M. S. Howarth, S. V. Searcy, and N. Kehtamavaz. 1990. A neural network for carrot tip classification. ASAE Paper No. 90-7549. St Joseph, Mich.: ASAE.
6. Chen, C., Y. P. Chiang, and Y. Pomeranz. 1989. Image analysis and characterization of cereal grains with a laser range finder and camera contour extractor. Cereal Chem. 66: 466-470.
7. Dowell, R E. 1993. Neural network classification of undamaged and damaged peanut kernels using spectral data. ASAE Paper No. 93-3050. St Joseph, Mich.: ASAE.
8. Keefe, P. D., and S. R. Draper. 1986. The measurement of new characters for cultivar identification in wheat using machine vision. Seed Sci. & Technol 14: 715-724.
9. Liao, K., M. R. Paulsen, J. F. Reid, B. C. Ni, and E. P. BonifacioMaghirang. 1993. Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of the ASAE 36(6): 1949-1953.
10. Luo, X. 1997. Color image analysis for cereal grain classification. Unpub. Ph.D. thesis. Winnipeg, Manitoba, Canada: Department of Biosystems Engineering, University of Manitoba.
11. Luo, X., D. S. Jayas, T. G. Crowe, and N. R. Bulley. 1997a. Evaluation of light sources for machine vision. Canadian Agric. Engng. 39(4): 309-315.
12. Luo, X., D. S. Jayas, and N. R. Bulley. 1997b. Identification of damaged kernels in wheat using a color machine vision system. ASAE Paper No. 97-3099. St Joseph, Mich.: ASAE.
13. Neuman, M., H. D. Sapirstein, E. Shwedyk, and W. Bushuk. 1987. Discrimination of wheat class and variety by digital image analysis of whole grain samples. J. Cereal Sci. 6: 125-132.
14. Rigney, M. P., and G. A. Kranzler. 1989. Seed classification performance of a neural network. ASAE Paper No. 89-7523. St. Joseph, Mich.: ASAE.
15. Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning internal representation by error propagation. In Parallel Distributed Processing, 1: 318-362, ed. D. E. Rumelhart et al. Cambridge, Mass.: MIT Press.
16. SAS. 1990. SAS User's Guide: Statistics. Raleigh, N.C.: Statistical Analysis System, Inc.
17. Sejnowski, T. J., and C. Rosenberg. 1987. Parallel networks that learn to pronounce English text. Complex Systems 1: 145-168.
18. Shatadal, P., D. S. Jayas, and N. R. Bulley. 1995. Digital image analysis for software separation and classification of touching grains. II. Classification. Transactions of the ASAE 38(2): 645-649.
19. Symons, S. J., and R. G. Fulcher. 1988a. Determination of wheat kernel morphological variation by digital image analysis: I. Variation in Eastern Canadian milling quality wheats. J. Cereal Sci. 8:211-218.
20. 1988b. Determination of wheat kernel morphological variation by digital image analysis: II. Variation in cultivais of soft white winter wheats. J. Cereal Sci. 88: 219-229.
21. Tesauro, G., and T. J. Sejnowski. 1989. A parallel network that learns to play backgammon. Artificial Intelligence 839:357-390.
22. Thai, C. N., J. N. Pease, and E. W. Tollner. 1991. Determination of green tomato maturity from X-ray computed tomography images. In P we. 1991 Symp. on Automated Agriculture for the 21st Century, 134-143. St. Joseph, Mich.: ASAE.
23. Zayas, L, Y. Pomeranz, and F. S. Lai. 1985. Discrimination between Arthur and Arkan wheats by image analysis. Cereal Chem. 62(2): 478-480.
24. Zayas, I., F. S. Lai, and Y. Pomeranz. 1986. Discrimination between wheat classes and varieties by image analysis. Cereal Chem. 63(1): 52-56.