Extraction of Morphological Features of Malaysian Rice Seed Varieties Using Flatbed Scanner

R. Ruslan, A. A Aznan, F.A Azizan, N. Roslan, N. Zulkifli


A high quality cultivated rice seeds are very important for Malaysian paddy industry to ensure a high yield of paddy production. Certified seeds that are mixed with other varieties and unwanted seeds such as weedy rice are considered as poor quality and faced rejection during a quality inspection by the Department of Agriculture. To ensure the seeds are cleaned from any foreign seeds, it is very important to develop a low cost and simple mechanism to classify the seeds according to its varieties. The use of a flatbed scanner is one of the alternative techniques for image acquisition of the seeds varieties. This study was carried out to evaluate morphological features of local rice seed varieties developed for Malaysian rice industry using image processing techniques. Image of four seed varieties, mainly are MR219, MR220, MR263, and MR269 were acquired and extracted using a normal desktop flatbed scanner. A LabVIEW program was developed to extract four main morphology features which are length, width, aspect ratio and rectangular aspect ratio. The extracted data were analysed in terms of its spread and variability. One-way ANOVA was done to compare the means of the morphological features. Further t-test analyses were done to distinguish between two seed varieties based on the variation in the morphological features of the seed kernel. The results indicated that seed length parameter extracted from the image acquired by the flatbed scanner is significant to differentiate the cultivated rice seed except for MR269 and MR220. Seed width can be used as a parameter to distinguish MR269 and MR220 pair. Thus, a combination of morphological parameters is necessary to classify the cultivated rice seed.


rice seed; image acquisition; machine vision; classification

Full Text:



Management and Delivery Unit (PEMANDU), “Economic Transformation Programme. Chapter 15 NKEA Agriculture Performanceâ€, Prime Minister’s Department of Malaysia, 2010

R. Labrada,“The need for improved weed management in rice,†in: Proceedings of the 20th Session of the International Rice Commision. Bangkok, Thailand, 2002

M. Azmi,S. Azlan, K. M. Yim, T.V. George, and S.E Chew, “Control of Weedy Rice in Direct-Seeded Rice Using Clearfield Production System in Malaysia.†Pak. K. Weed Sci. Res., vol 18, pp. 49-53, 2012

Department of Agriculture Standard (SJPM-2009) Specification for Rice Seed Production, Department of Agriculture Malaysia, 2011

A. Ou Yang, R. Gao, Y. Liu, X. Sun, Y. Pan and X. Dong, “An Automatic Method for Identifying Different Variety of Rice Seeds Using Machine Vision Technologyâ€. ICNC, pp 84-88, 2010

S.G Harish and M. Siddappa, “A method for Identification of Basmati Rice Grain of India and Its Quality Using Pattern Classification.†International Journal of Engineering Research and Application (IJERA) Vol 3(1), pp 268-273, 2013

P.T.T. Hong, T.T Than-Hai, L. Thi-Lan, V.T. Hoang, V. Hai,and T.T. Nguyen, “Comparative Study on Vision Based Rice Seed Varieties Identification,†7th Int. Conf. on Knowledge and Systems Eng., IEEE, pp 377-382, 2015

B. Lurstwut, & C. Pornpanomchai, “Plant seed image recognition system.â€Int. J. of Eng. and Tech., vol 3(6), pp. 600-605, 2011

B. Lurstwut, & C. Pornpanomchai, “Application of Image Processing and Computer Vision on Rice Seed Germination Analysis,†Int. J. of App. Eng. Research, vol 11 (9), pp. 6800-6807, 2016.

A.A. Aznan, I.H. Rukunudin, A.Y.M. Shakaff, R. Ruslan, A. Zakaria and F.S.A. Saad. “Application of Image Processing Technique to Extract Morphological Characteristics of Weedy Rice Seeds Variants for Malaysian Seed Industryâ€. Adv. Environ. Biol., vol 8(22), pp. 112-115, 2014

“IMAQ Vision for LabVIEW User Manual,â€National Instruments Corp, Austin, Texas, 2000

Yuhandri, M. Sarifuddin, W. Eri Prasetyo and Karmilasari,"Object Feature Extraction of Songket Image Using Chain Code Algorithm, "International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 235-241, 2017.

Jufriadif Na`am, Johan Harlan, Sarifuddin Madenda and Eri Prasetio Wibowo, “The Algorithm of Image Edge Detection on Panoramic Dental X-Ray using Multiple Morphological Gradient (mMG) Methodâ€, International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1012-1018, 2016.

F.G. Hernandez and J.G. Gil, “A machine vision system for classification of wheat and barley grain kernelsâ€. Spanish Journal of Agricultural Research, vol 9(3), pp. 672-680, 2011

Y. Liu,C. Fang,Y. B. Yi, and R.Q. Xin, “Identification of rice seed varieties using neural network.â€Journal of Zhejiang Uni. Science, Vol 6b(11), pp.1095-1100, 2005

I. Alias, O. Othman, H. Mohamad, A. Saad, H. Habibuddin, A.B Abd Rahman, and S. Azlan. “New Rice Variety MR220â€. Buletin Teknologi Tanaman, vol 2, pp 7-13, 2005

A.A. Aznan, I.H. Rukunudin, A.Y.M. Shakaff, R. Ruslan, A. Zakaria and F.S.A. Saad. “The use of machine vision technique to classify cultivated rice seed variety and weedy rice seed variants for the seed industryâ€. Int. Food Research Journal, vol 23(suppl), pp. S31-S35, 2016.

DOI: http://dx.doi.org/10.18517/ijaseit.8.1.2752


Published by INSIGHT - Indonesian Society for Knowledge and Human Development