Physical Quality Investigation of Germinated Brown Rice by using Image Processing

Main Article Content

Sarawoot Boonkidram
Nattavut Sriwiboon

Abstract

In this paper, we use the deep learning image processing technology and the CNN algorithm to investigate the quality of germinated brown rice. We compile the germinated brown rice digital images 500 samples of germinated brown rice and non-germinated brown rice, which classify by the experts and the folk philosopher. After that, we develop the program and use the images of the germinated brown rice 250 samples, and the images of non-germinated brown rice 250 samples to create a model for classifying quality the germinated brown rice. The results that are shown the use of CNN algorithms for creating a model demonstration the exceptional accuracy at 95.17%. Then, we develop the program to test the model that shown an accuracy of classifying the quality of germinated brown rice as high as 96%, including the classifying quality of non-germinated brown rice as accuracy of 84%.

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How to Cite
[1]
S. Boonkidram and N. Sriwiboon, “Physical Quality Investigation of Germinated Brown Rice by using Image Processing”, JIST, vol. 10, no. 2, pp. 101-109, Dec. 2020.
Section
Research Article: Multidisciplinary (Detail in Scope of Journal)

References

[1] Shoichi I, Marketing of Value-Added Rice Production in Japan: Geminated Brown Rice and Rice Bread, In Rice in Global Markets, UN Food in Global Markets, Italy, 2004.

[2] Kayahara, H. and K. Tsukahara, Flavor, health and nutritional quality of pre-germinated brown rice, International Chemical Congress of Pacific Basin Societies in Hawaii, 2000.

[3] J. Jaroenjit, A. Panpanasakul, P. Chaisri, P. Promduang, and S. Prompongusawa, “Classification pearls using image processing,” Proceedings of the 9th Hatyai National and International Conference, Thailand, pp. 1679 - 1691, 2014.

[4] A. Tungkastan, and K. Leewun, “Pixel-Based Car Model Detection and Recognition,” Engineering Journal of Siam University, Vol. 19, January-June, pp. 90–102, 2018.

[5] S. Sarraf, and G. Tofighi, “A hybrid sequential feature selection approach for the diagnosis of Alzheimer's Disease,” International Joint Conference on Neural Networks (IJCNN), 24-29 July, pp. 1216-1220, 2016.

[6] E. Humphrey, and J. Bello, “Rethinking Automatic Chord Recognition with Convolution Neural Networks,” Proceedings of the 11th International Conference on Machine Learning and Application, 2012.

[7] T. Tathawee, S. Prasarnpun, S. Onbua, T. Pinthong, and A. Suwannakom, “Orchid identification based on computer vision analysis,” Proceedings of the 6th National Science Research Conference, Thailand, pp. 47- 56, 2014.

[8] B. Tilmann, “The Business Impact of Predictive Analytics,” ed. IGI Global, September - December 2007.

[9] R. Kohavi, “A study of crossvalidation and bootstrap for accuracy estimation and model selection,” Proceedings of the Fourteenth International joint conference on Artificial Intelligence, Montreal, Canada, pp. 1137-1143, 1995.

[10] Yang Lu, Shujuan Yi, Nianyin Zeng, Yurong Liu and Yong Zhang. “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, Vol. 267, No. 6, December, pp. 378-384, 2017.

[11] Ronnel R. Atole, and Daechul Park, “A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies,” International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 9, No. 1, pp. 67-70, 2018.

[12] B. Lurstwut, and C. Pornpanomchai, “Application of Image Processing and Computer Vision on Rice Seed Germination Analysis,” International Journal of Applied Engineering Research, Vol. 11, November, pp. 6800-6807, 2016.

[13] Jose D Guzman, and Engelbert K. Peralta, “Classification of Philippine Rice Grains Using Machine Vision and Artificial Neural Networks,” World Conference on Agricultural Information and IT, IAALD AFITA WCCA, pp. 41-48, 2008.

[14] OuYang, A., Gao, R., Liu, Y., Sun, X., Pan, Y., Dong, X, “An Automatic Method for Identifying Different Variety of Rice Seeds Using Machine Vision Technology,” Proceeding of the Sixth International Conference on Natural Computation 1, pp. 84-88, 2010.

[15] Shantaiya, S., and Ansari, U, “Identification Of Food Grains And Its Quality Using Pattern Classification,” International Journal of Computer and Communication Technology (IJCCT), Vol. 2, No.2, pp. 3-5, 2010.

[16] Gujjar, H.S., Siddappa, M, “A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification,” International Journal of Engineering Research and Applications (IJERA), Vol. 3, No. 1, pp. 268-273, 2013.

[17] Chetana V. Maheshwari, Kavindra R. Jain, and Chintan K. Modi, “Non-destructive Quality Analysis of Indian Gujarat-17 Oryza Sativa SSP Indica (Rice) Using Image Processing,” International Journal of Computer Engineering Science (IJCES), Vol. 2 Issue 3, March, 2012.

[18] LIU Zhao-yan, CHENG Fang, YING Yi-bin and RAO Xiu-qin, “Identification of rice seed varieties using neural network,” Journal of Zhejiang University SCIENCE. Vol. 6B,(11), pp. 1095-1100, 2005.

[19] MIROLYUB MLADENOV and MARTIN DEJANOV, “Application of Neural Networks for Seed Germination Assessment,” 9th WSEAS International Conference on NEURAL NETWORKS (NN’08), Sofia, Bulgaria, May 2-4, pp. 67-72, 2008.

[20] Xiao Chena, Yi Xun, Wei Li and Junxiong Zhang, “Combining discriminant analysis and neural networks for corn variety identification,” Computers and Electronics in Agriculture. 715, pp. 548–553, 2010.