Detection of stress cracks in rice kernels based on machine vision

AMA, Agricultural Mechanization in Asia, Africa and Latin America

Volumn 40, Issue 4, 2009, Pages 38-41

  Lizhang, Xu ^a   Yaoming, Li ^a  

^a JIANGSU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING ALGORITHM; MACHINE VISION SYSTEMS; MULTIPLE CRACK; MULTIPLE STRESS; ON-MACHINES; PROCESSING TIME; RICE KERNELS; SINGLE CRACK; STRESS CRACKS;

COMPUTER VISION; GRAIN (AGRICULTURAL PRODUCT);

CRACK DETECTION;

EID: 77949549404     PISSN: 00845841     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Delwiche, M. J., S. Tang, J. F. Thompson. 1990. Prune defect detection by line2scan imaging. Transactions of the ASAE, 33(3): 950-954.
2. Rigney, M. P., G. H. Brusewitz, G, A. Kranzle. 1992. Asparagus defect inspection with machine vision. Transactions of the ASAE, 35(6): 1873-1878.
3. YING, Yi Bin. 2000. Study on background segment and edge detection of fruit image using ma-chine vision. Journal of Zhejiang University (Agric. & Life Sci.), 26(1): 35-38.
4. Shahin, M. A., E. W. Tollner, R. D. Gitaitis, et al., 2002. Classification of sweet onions based on internal defects using image processing and neural network techniques. Transactions of the ASAE, 45(5): 1613-1618.
5. Shanhin, M. A., E. W. Tollner, R. W. McClendon, et al, 2002. Ap-ple classification based on surface bruises using image processing and neural networks. Transactions of the ASAE, 45(5): 1619-1627.
6. Yang, Fuzeng, Zheng Wang, Went-ing Han, et al., 2005. Wavelet transform based multi-scale edge detection of dehiscence furrow in Chinese date images. Transac-tions of the CSAE, 21(6): 92-95.
7. Wu, Guifang, Ke Xu, Jinwu Xu, et al., 2006. Application of morpho-logical wavelet in surface crack defect detection of medium plates. Journal of University of Science and Technology Beijing, 28(6): 591-594.
8. Yang, Zhang, Jianzheng Chen. 2003. Wavelet demising and multi-scale edge detection ultrasonic image. NDT. 25(6): 279-282.
9. Dauda, A., A. N. Aviara. 2001. Effect of threshing methods on maize grain damage and viability. AMA, 32(4): 43-46.
10. Panigrahi, S., M.K. Misra, C. Bern, et al., 1995. Background segmen-tation and dimensional measure-ment of corn germplasm. Trans, of the ASAE, 38(1): 291-297.
11. Ke, Liao. 1993. A knowledge-based machine vision system for grain quality inspection (PhD). University of Illinois at Urbana-Champaign.
12. Steenhoek, Loren W. 1999. A proba-bilistic neural network computer vision system for corn kernel damage evaluation (PhD). Iowa State University, Ames Iowa.
13. Yie, T. J., K. Liao, M. R. Paulsen, J..F. Reid, E. B. Maghirang. 1993. Corn Kernel stress cracks detec-tion by machine vision. ASAE Meeting, Paper No. 93-3526.
14. Chulsoo, Kim. 1991. Detection of stress cracks in corn kernels using machine vision (PhD). University of Illinois at Urbana-Champaign.
15. Mallat, S. G., S. Zhong. 1992. Characterization of Signals from Multi-scale Edges. IEEE Trans on Pattern Analysis and Machine Intelligence (S0162-8828), 14(7): 710-732.
16. Mallat, S, W. Hwang. 1992. Singu-larity Detection and Processing with Wavelets. IEEE Transaction on Information Theory (S00189448), 38(2): 617-643.