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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.11 P.1095-1100

http://doi.org/10.1631/jzus.2005.B1095


Identification of rice seed varieties using neural network


Author(s):  LIU Zhao-yan, CHENG Fang, YING Yi-bin, RAO Xiu-qin

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

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

Key Words:  Machine vision, Digital image processing, Neural network, Rice seeds, Classification


LIU Zhao-yan, CHENG Fang, YING Yi-bin, RAO Xiu-qin. Identification of rice seed varieties using neural network[J]. Journal of Zhejiang University Science B, 2005, 6(11): 1095-1100.

@article{title="Identification of rice seed varieties using neural network",
author="LIU Zhao-yan, CHENG Fang, YING Yi-bin, RAO Xiu-qin",
journal="Journal of Zhejiang University Science B",
volume="6",
number="11",
pages="1095-1100",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.B1095"
}

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%T Identification of rice seed varieties using neural network
%A LIU Zhao-yan
%A CHENG Fang
%A YING Yi-bin
%A RAO Xiu-qin
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 11
%P 1095-1100
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B1095

TY - JOUR
T1 - Identification of rice seed varieties using neural network
A1 - LIU Zhao-yan
A1 - CHENG Fang
A1 - YING Yi-bin
A1 - RAO Xiu-qin
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 11
SP - 1095
EP - 1100
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B1095


Abstract: 
A digital image analysis algorithm based color and morphological features was developed to identify the six varieties (ey7954, syz3, xs11, xy5968, xy9308, z903) rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis. Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xs11, xy5968, xy9308, z903 respectively.

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

Reference

[1] Draper, S.R., Travis, A.J., 1984. Preliminary observations with a computer based system for analysis of the shape of seeds and vegetative structures. J. Nat. Inst. Agric. Botany, 16(3):387-395.

[2] Hawk, A.L., Kaufmann, H.H., Watson, C.A., 1970. Reflectance characteristics of various grain. Cereal Sci. Today, 15(11):381-384.

[3] Huang, X.Y., Li, J., Jiang, S., 2004. Study on identification of rice varieties using computer vision. Journal of Jiangsu University (Natural Science Edition), 25(2):102-104 (in Chinese).

[4] Keefe, P.D., 1992. A dedicated wheat grading system. Plant Varieties & Seeds, 5:27-33.

[5] Lai, F.S., Zayas, I., Pomeranz, Y., 1986. Application of pattern recognition techniques in the analysis of cereal grains. Cereal Chemistry, 63(2):168-174.

[6] Luo, X., Jayas, D.S., Symons, S.J., 1999. Identification of damaged kernels in wheat using a color machine vision. Journal of Cereal Science, 30(1):45-59.

[7] Majumdar, S., Jayas, D.S., 2000. Classification of cereal grains using machine vision. IV. Combined morphology, color and texture models. Trans. ASAE, 43(6):1689-1694.

[8] Majumdar, S., Jayas, D.S., Hehn, J.L., Bulley, N.R., 1996. Classification of various grains using optical properties. Canadian Agric. Eng., 38(2):139-144.

[9] Myers, D.G., Edsall, K.J., 1989. The application of image processing techniques to the identification of Australian wheat varieties. Plant Var. & Seeds, 2(2):109-116.

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

[11] Neuman, M., Sapirstein, H.D., Shwedyk, E., Bushuk, W., 1989a. Wheat grain color analysis by digital image processing: I. Methodology. J. Cereal Sci., 10:175-182.

[12] Neuman, M., Sapirstein, H.D., Shwedyk, E., Bushuk, W., 1989b. Wheat grain color analysis by digital image processing: II. Wheat class determination. J. Cereal Sci., 10:183-192.

[13] 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(1):3-14.

[14] Symons, S.J., Fulcher, R.G., 1988a. Determination of wheat kernel morphological variation by digital image analysis. I. Variation in Eastern Canadian milling quality wheats. J. Cereal Sci., 8(3):211-218.

[15] Symons, S.J., Fulcher, R.G., 1988b. Determination of wheat kernel morphological variation by digital image analysis. II. Variation in cultivars of soft white winter wheats. J. Cereal Sci., 8(3):219-229.

[16] Travis, A.J., Draper, S.R., 1985. A computer based system for the recognition of seed shape. Seed Sci. & Technol., 13:813-820.

[17] Zayas, I., Lai, F.S., Pomeranz, Y., 1986. Discrimination between wheat classes and varieties by image analysis. Cereal Chemistry, 63(1):52-56.

[18] Zhang, Y.J., 1999. Image Processing and Analysis. Tsinghua University Press, Beijing, China, p.20 (in Chinese).

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