Classifying paddy rice by morphological and color features using machine vision

Cereal Chemistry

Volumn 82, Issue 6, 2005, Pages 649-653

  Liu, Chang Chun ^a   Shaw, Jai Tsung ^a   Poong, Keen Yik ^a   Hong, Mei Chu ^b   Shen, Ming Lai ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b Taichung District Agricultural Improvement Station   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

BACK-PROPAGATION NEURAL NETWORKS; CALIBRATED MODEL; CLASSIFICATION RATES; COLOR FEATURES; FEATURES SELECTION; FIRST PRINCIPAL COMPONENTS; MACHINE-VISION; MORPHOLOGICAL FEATURES; PADDY-RICE; RICE CULTIVARS;

COMPUTER VISION;

EID: 27744483903     PISSN: 00090352     EISSN: None     Source Type: Journal    
DOI: 10.1094/CC-82-0649     Document Type: Article

References (13)

1. Barker, D. A., Vuori, T. A., and Myers, D. C. 1992. The use of slice and aspect ratio parameters for the discrimination of Australian wheat varieties. Plant Varieties Seeds 5:47-52.
2. Burks, T. F., Shearer, S. A., Gates, R. S., and Donohue, K. D. 2000. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans. ASAE 43:1029-1037.
3. Chang, H. W., Shaw J. T., Lee, S. M., and Horng, M. J. 2000. To classify the varieties of paddy rice by near infrared and image processing techniques. J. Agric. Machinery 9(4):1-16.
4. Luo, X., Jayas, D. S., and Symons, S. J. 1999. Comparison of statistical and neural network methods for classifying cereal grains using machine vision. Trans. ASAE 42:413-419.
5. Majumdar, S., and Jayas, D. S. 2000. Classification of cereal grains using machine vision: Combined morphology, color, and texture models. Trans. ASAE 43:1689-1694.
6. Paliwal, J., Visen, N. S., and Jayas, D. S. 2001. Evaluation of neural network architectures for cereal grain classification using morphological features. J. Agric. Eng. Res. 79:361-370.
7. Paliwal, J., Visen, N. S., Jayas, D. S., and White, N. D. G. 2003. Comparison of a neural network and a nonparametric classifier for grain kernel identification. Biosystems Eng. 85:405-413.
8. Shatadal, P., Jayas, D. S., and Bulley, N. R. 1995. Digital image analysis for software separation and classification of touching grains. II. Classification. Trans. ASAE 38:645-649.
9. Shen, M.-L. 2003. Introduction to Statistics. 4th Ed. Pages 376-390. (In Chinese) Chiu-chow Publisher: Taipei, Taiwan.
10. Travis, A. J., and Draper, S. R. 1985. A computer based system for the recognition of seed shape. Seed Sci. Technol. 14:715-724.
11. Tsang, M., Hsia, I., and Chen, C. 1990. Identification of rice cultivars by means of quantifying the grain shape. College of Agriculture, National Taiwan University: Taiwan.
12. Wang, D., Dowell, F. E., and Lacey, R. E. 1999. Single wheat kernel color classification using neural networks. Trans. ASAE 43:233-240.
13. Zayas, I., Pomeranz, Y., and Lai, F. S. 1986. Discrimination between wheat classes and varieties by image analysis. Cereal Chem. 63:52-56.