Determining vitreousness of durum wheat using transmitted and reflected images

Transactions of the American Society of Agricultural Engineers

Volumn 48, Issue 1, 2005, Pages 219-222

  Wang, N ^a   Zhang, N ^b   Dowell, F E ^c   Pearson, T ^c  

^a MCGILL UNIVERSITY   (Canada)

^b Sunflower Networking Group   (United States)

^c KANSAS STATE UNIVERSITY   (United States)

Author keywords

Automation; Grading; Illumination; Inspection; Machine vision; Wheat

Indexed keywords

GRAIN (AGRICULTURAL PRODUCT); IMAGE PROCESSING; MATHEMATICAL MODELS; NEURAL NETWORKS;

DIGITAL IMAGING TECHNOLOGY; DURUM WHEAT; INSPECTION SYSTEMS; NEURAL NETWORK MODELS;

FOOD PRODUCTS;

COMPUTER VISION; DIGITAL IMAGE;

GRAIN; IMAGE ANALYSIS; MATHEMATICAL MODELS; NEURAL NETWORKS; WHEAT;

TRITICUM AESTIVUM; TRITICUM TURGIDUM SUBSP. DURUM;

EID: 18644373393     PISSN: 00012351     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Abaye, A. O., D. E. Brann, M. M. Alley, and C. A. Griffey. 1997. Winter Durum Wheat: Do We Have All the Answers? Pub. No. 424-802. Blacksburg, Va.: Virginia Tech, Virginia Cooperative Extension.
2. Bacci, L., B. Rapi, F. Colucci, and P. Novaro. 2002. Durum wheat quality evaluation software. In Proc. World Congress of Computers in Agriculture and Natural Resources, 49-55. St. Joseph, Mich.: ASAE.
3. Dowell, F. E. 2000. Differentiating vitreous and nonvitreous durum wheat kernels by using near-infrared spectroscopy. Cereal Chemistry 77(2): 155-158.
4. Luo, X., D. S. Jayas, and S. J. Symons. 1999. Identification of damaged kernels in wheat using a color machine vision system. J. Cereal Science 30(1): 49-59.
5. Ruan, R., S. Ning, L. Luo, P. Chen, R. Jones, W. Wilcke, and V. Morey. 2001. Estimation of weight percentage of scabby wheat kernels using an automatic machine vision and neural network based system. Trans. ASAE 44(4): 983-988.
6. Sissons, M. J., B. G. Osborne, R. A. Hare, S. A. Sissons, and R. Jackson. 2000. Application of the single-kernel characterization system to durum wheat testing and quality prediction. Cereal Chemistry 77(2): 4-10.
7. Symons, S. J., L. Van Schepdael, and J. E. Dexter. 2003. Measurement of hard vitreous kernels in durum wheat by machine vision. Cereal Chemistry 80(5): 511-517.
8. Wang, N., F. E. Dowell, and N. Zhang. 2003. Determining wheat vitreousness using image processing and a neural network. Trans. ASAE. 46(4): 1143-1150.
9. Zayas, I. Y, D. B. Bechtel, J. D. Wilson, and E. E. Dempster. 1994. Distinguishing selected hard and soft red winter wheat by image analysis of starch granules. Cereal Chemistry 71(1): 82-86.