Chinese Date Grading Based on Computer Vision

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Abstract:

In order to implement the accuracy and robust classification of Chinese dates according to size and color based on computer vision techniques on line, the method of classification according to size and color for Chinese date was studied. Taking the black rollers as background, the Chinese date images were pre-segmented by double thresholds in RGB color space. Through morphological operation, contour trace and region fill, the whole Chinese date target was obtained. the maximum diameter value was used to be the character value for size classification. The difference of saturation and hue of pericarp area in HIS color space was the color grading criteria. The results indicated that the accuracy of diameter measurement is 1.92mm, Experiment results proved the methods is effective to classify Chinese date by size and shape.

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Advanced Materials Research (Volumes 838-841)

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3283-3286

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November 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Brosnan T, Sun D W. Improving quality inspection of food products by computer vision—a review[J]. Journal of Food Engineering, 2004, 61(1): 3~16.

DOI: 10.1016/s0260-8774(03)00183-3

Google Scholar

[2] Miller W M, Drouillard G P. Multiple feature analysis for machine vision grading of Florida citrus[J]. Applied Engineering in Agriculture, 2001, 17(5): 627~633.

DOI: 10.13031/2013.6900

Google Scholar

[3] Liang Weijie, Deng Jizhong, Zhang Tailing. Study on computer vision inspecting for pear surface defect[J]. Transactions of the Chinese Society for Agricultural Machinery, 2005, 36(7): 101~103.

Google Scholar

[4] Ying Yibin, Rao Xiuqin, Ma Junfu. Methodology for nondestructive inspection of citrus maturity with machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2004, 20(2): 144~147.

Google Scholar

[5] Laylin, S V, Alchanatis, E Fallik et al. Image-Processing Algorithms for Tomato Classification[J]. Transactions of the ASAE 2002, 45(3): 851~858.

DOI: 10.13031/2013.8838

Google Scholar

[6] Bundit Jarimopas, Nitipong Jaisin. An experimental machine vision system for sorting sweet tamarind[J]. Journal of Food Engineering. 2008, 89: 291–297.

DOI: 10.1016/j.jfoodeng.2008.05.007

Google Scholar

[7] Li Xiuzhi. Study on apple shape grading system based on machine vision [D]. Nanjing agriculture university, (2003).

Google Scholar

[8] Lin Kaiyan, Wu Junhui, Xu Lihong. Separation approach for shape grading of fruits using computer vision [J]. Transactions of the Chinese Society for Agricultural Machinery, 2005, 36(6): 71~74.

Google Scholar

[9] Ying Yibin, Gui Jiangsheng, Rao Xiuqin. Fruit shape grading based on Zernike [J]. Journal of Jiangsu University: Natural Science Edition, 2007, 28(1): 1~3.

Google Scholar

[10] Feng Bin, Wang Maohua. Detecting method of fruit size based on computer vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2003, 34(1): 73~75.

Google Scholar

[11] zhang Junxiong. Restoration of motion blurred images in citrus grading based on machine vision [D]. Beijing: China agricultural university, (2007).

Google Scholar