Full Text:   <3436>

CLC number: TP39

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 0000-00-00

Cited: 5

Clicked: 5645

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.6 P.663-667

http://doi.org/10.1631/jzus.2004.0663


Machine vision inspection of rice seed based on Hough transform


Author(s):  CHENG Fang, YING Yi-bin

Affiliation(s):  College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   fcheng@zju.edu.cn

Key Words:  Hough transform, Incompletely closed glumes, Rice seed, Machine vision


Share this article to: More

CHENG Fang, YING Yi-bin. Machine vision inspection of rice seed based on Hough transform[J]. Journal of Zhejiang University Science A, 2004, 5(6): 663-667.

@article{title="Machine vision inspection of rice seed based on Hough transform",
author="CHENG Fang, YING Yi-bin",
journal="Journal of Zhejiang University Science A",
volume="5",
number="6",
pages="663-667",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0663"
}

%0 Journal Article
%T Machine vision inspection of rice seed based on Hough transform
%A CHENG Fang
%A YING Yi-bin
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 6
%P 663-667
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0663

TY - JOUR
T1 - Machine vision inspection of rice seed based on Hough transform
A1 - CHENG Fang
A1 - YING Yi-bin
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 6
SP - 663
EP - 667
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.0663


Abstract: 
A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Casady, W.W., Paulsen, M.R., Reid, J.F., Sinclair, J.B., 1992. A trainable algorithm for inspection of soybean seed quality.Trans. ASAE,35(6):2027-2034.

[2] Gunasekaran, S., Cooper, T.M., Berlage, A.G., 1988a. Evaluating quality factors of corn and soybean using a computer vision system.Trans. ASAE,31(4):1264-1271.

[3] Gunasekaran, S., Cooper, T.M., Berlage, A.G., 1988b. Soybean seed coat and cotyledon crack detection by image processing.J. Agric. Eng. Res.,41:139-148.

[4] Lai, F.S., Zayas, I., Pomeranz, Y., 1986. Application of pattern recognition techniques in the analysis of cereal grains.Cereal Chem.,63(2):168-172.

[5] Liao, K., Paulsen, M.R., Reid, J.F., Ni, B., Bonifacio-Maghirang, E.P., 1993. Corn kernel breakage classification by machine vision.Trans. ASAE,36(6):1949-1953.

[6] Ng, H.F., Wilcke, W.F., Morey, R.V., Lang., J.P., 1998. Machine vision evaluation of corn kernel mechanical and mold damage.Trans. ASAE,41(2):425-420.

[7] Neuman, M., Sapirstein, H.D., Shwedyck, E., Bushuk, W., 1987. Discrimination of wheat class and variety by digital image analysis of whole grain samples.J. Cereal Sci.,6:125-132.

[8] Sapirstein, H.D., Neuman, M., Wright, E.H., Shwedyk, E., Bushuk, W., 1987. An instrumental system for cereal grain classification using digital image analysis.J. Cereal Sci.,6:3-14.

[9] Satake, T., Furuya, T., Shimohara, T., 1992. Study on the development of neuroprocessor for the quality evaluation of brown rice.J. Japanese Soc. Agric. Machinery,54(4):67-75.

[10] Zayas, I., Pomeranz, L.Y., Lai, F.S., 1985. Discrimination between Arthur and Arkan wheats by image analysis.Cereal Chem.,62:478.

[11] Zayas, I., Converse, H., Steele, J., 1990. Discrimination of Whole from broken corn kernels with image analysis.Trans. ASAE,33(5):1642-1646.

[12] Zayas, I., Martin, C.R., Steele, J.L., Katsevich, A., 1996. Wheat classification using image analysis and crush-force parameters.Trans. ASAE,39:2199-2204.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE