Identification of defect Pleurotus Geesteranus based on computer vision

被引：0
|
作者：
Huang X. [1 ]
Jiang S. [1 ]
Chen Q. [1 ]
Zhao J. [1 ]
机构：
[1] School of Food and Biological Engineering, Jiangsu University
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2010年 / 26卷 / 10期
关键词：
Identification; Image processing; Pleurotus Geesteranus; Support vector machines(SVM);
D O I：
10.3969/j.issn.1002-6819.2010.10.058
中图分类号：
学科分类号：
摘要：
An identification method was developed to automatically recognize defect Pleurotus Geesteranus based on computer image processing technology. Seven feature parameters were extracted acording to investigation of shapes of Pleurotus Geesteranus, which were fractal dimension, relative length, roundness, shape factor, convexity of the pileus, aspect ratio, and crooked degree of the stipe. Subsequently, four feature parameters were further extracted by stepwise linear regression, which were fractal dimension, relative length, aspect ratio, and crooked degree of the stipe. Finally, support vector machine classifier was employed to build discrimination model, where four feature parameters selected were used as inputs vector. Recognition rate of model was 96.67%, when discrimination model was tested by some independent samples in the prediction set. This study demonstrates that it is feasible to identify defect Pleurotus Geesteranus using machine vision technique and provide technical support for on-line grading of Pleurotus Geesteranus.
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页码：350 / 354
页数：4
相关论文
共 15 条
  • [1] Zhang J., Huang C., Zheng S., A new variety of Pleurotus - The characteristics and features of Pleurotus Geesteranus, Edible fungi of China, 24, 4, pp. 25-26, (2005)
  • [2] Riquelme M.T., Barreiro P., Ruiz-Altisent M., Et al., Olive classification according to external damage using image analysis, Journal of Food Engineering, 87, 3, pp. 371-379, (2008)
  • [3] Blasco J., Cubero S., Gomez-Sanchis J., Et al., Development of a machine for the automatic sorting of pomegranate(Punica granatum) arils based on computer vision, Journal of Food Engineering, 90, 1, pp. 27-34, (2009)
  • [4] Cho N.-H., Chang D.-I., Lee S.-H., Et al., Development of automatic sorting system for green pepper using machine vision, Korea: ASABE Annual International Meeting, pp. 1-12, (2007)
  • [5] Kondo N., Kamata J., Ninomiya K., Et al., Rotary tray for machine vision inspection of whole eggplant fruit, USA, ASABE Annual International Meeting, pp. 1-12, (2006)
  • [6] Yang F., Zhu S., Qiu Q., Prickly ash appearance quality detection based on computer vision and its implementation in MATLAB, Transactions of the CSAE, 24, 1, pp. 198-202, (2008)
  • [7] Zheng L., Yang X., Xu G., Et al., Non destructive detection of egg freshness based on computer vision, Transactions of the CSAE, 25, 2, pp. 335-339, (2005)
  • [8] Zhan H., Li X., Wang W., Et al., Determination of chestnuts grading based on machine vision, Transactions of the CSAE, 26, 4, pp. 327-331, (2010)
  • [9] Feng B., Wang M., Computer vision classification of fruit based on fractal color, Transactions of the CSAE, 18, 2, pp. 141-144, (2002)
  • [10] Peng R., Xie H., Ju Y., Computer method of fractal dimension for 2-D digital image, Journal of China University of Mining and Technology, 33, 1, pp. 19-24, (2004)
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