Abstract
This paper proposed a detection method of rice process quality using the color and BP neural network. A rice process quality detection device based on computer vision technology was designed to get rice image, a circle of the radius R in the abdomen of the rice was determined as a color feature extraction area, and which was divided into five concentric sub-domains by the average area, the average color of each sub-region H was extraction as the color feature values described in the surface process quality of rice, and then the 5 color feature values as input values were imported to the BP neural network to detection the surface process quality of rice. The results show that the average accuracy of this method is 92.50% when it was used to detect 4 types of rice of different process quality.
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Hou, C., Seiichi, O., Yasuhisa, S., et al.: Application of 3D-Microslicing image processing system in rice quality evaluation. Transactions of The Chinese Society of Agricultural Engineering 17(3), 92–95 (2001)
Ling, Y., Wang, Y., Sun, M., et al.: A machine vision based instrument for rice appearance quality. Transactions of The Chinese Society of Agricultural Machinery 36(9), 89–92 (2005)
Wan, Y.N., Lin, C.M.: Rice quality classification using an automatic grain quality inspection system. Transaction of ASAE 45(2), 379–387 (2002)
Abdullah, M.Z., Guan, L.C., Lim, K.C.: The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. Journal of Food Engineering (61), 125–135 (2004)
Shang, Y., Hou, C., Chang, G.: Automatic detection of yellow-colored rice using image recognition. Transactions of the Chinese Society of Agricultural Engineering 20(4), 146–148 (2004)
Cai, J.: An analysis of color models and criteria for their application to quality test of farm products. Journal of JiangSu University of Science and Technology 18(5), 22–25 (1997)
Vizhanyo, T., Felfoldi, J.: Enhancing color differences in images of diseased mushrooms. Computers and Electronics in Agriculture 26(2), 187–198 (2000)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagation errors. Nature (323), 533–536 (1986)
Majumdar, S., Jayas, D.S.: Classifieation of cereal grains using machine vision: Morphology models. Trans. of the ASAE 43(6), 1669–1675 (2000)
Nakano, K.: Application of neural networks to the color grading of apples. Computers and Electronics in Agriculture 18, 105–116 (1997)
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© 2011 IFIP International Federation for Information Processing
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Wan, P., Long, C., Huang, X. (2011). A Detection Method of Rice Process Quality Based on the Color and BP Neural Network. In: Li, D., Liu, Y., Chen, Y. (eds) Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, vol 344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18333-1_4
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DOI: https://doi.org/10.1007/978-3-642-18333-1_4
Publisher Name: Springer, Berlin, Heidelberg
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