Determination of chestnuts grading based on machine vision

被引：0
|
作者：
Zhan H. [1 ]
Li X. [1 ]
Wang W. [1 ]
Wang C. [1 ]
Zhou Z. [1 ]
Huang Y. [1 ]
机构：
[1] College of Engineering, Huazhong Agricultural University
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2010年 / 26卷 / 04期
关键词：
Agricultural products; Chestnut; Image processing; Machine vision; Neural networks;
D O I：
10.3969/j.issn.1002-6819.2010.04.056
中图分类号：
学科分类号：
摘要：
In order to realize grading of eligible and defected chestnut by using machine vision, a classification method of chestnut was developed based on BP-ANN and image feature of chestnut. In this experiment, Luotian chestnuts were used as experimental targets. Principal component analysis (PCA) was implemented on these feature variables from eight eigen values including color parameters and veins characteristics parameters etc., and principal components (PCs) vectors were extracted as the inputs of pattern recognition. Grading models were built by BP neural network. The test result showed that when the number of principal component factor was three and the number of nodes of hidden layer was twelve, the discriminating rate was as high as 100% in training set, and 91.67% in prediction set. The overall results shows that it is feasible to discriminate chestnut quality with machine vision.
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收藏
页码：327 / 331
页数：4
相关论文
共 19 条
  • [1] Gao H., Li C., Cai J., Advances on the study of natural fresh-keeping agents for fruits and vegetables, Journal of Chinese Institute of Food Science and Technology, 3, 1, pp. 86-91, (2003)
  • [2] Kawamura S., Natsuga M., Takekura K., Et al., Development of automatic rice-quality inspect-ion system, Computers and Electronics in Agriculture, 40, 1-3, pp. 115-126, (2003)
  • [3] Ninomiya K., Kondo N., Chong V.K., Et al., Machine vision systems of eggplant grading system, Automation Technology for Off-Road Equipment
  • [4] Li J., Xue L., A study on navel orange grading system based on computer vision, Acta Agriculturae Universitatis Jiangxiensis, 28, 2, pp. 304-307, (2006)
  • [5] Tao Y., Heinemann P.H., Varghese Z., Et al., Machine vision for color inspection of potatoes and apples, Trans of the ASAE, 38, 5, pp. 1555-1561, (1995)
  • [6] Zhang S., Chen X., Zhang X., Image processing system for detecting and grading apple, peach and other agricultural products, Transactions of the CSAE, 15, 1, pp. 201-204, (1999)
  • [7] Feng B., Wang M., Computer vision classification of fruit based on fractal color, Transactions of the CSAE, 18, 2, pp. 141-144, (2002)
  • [8] Cheng F., Ying Y., Inspection of mildewed rice seeds based on color feature, Transactions of the Chinese Society for Agricultural Machinery, 35, 4, pp. 102-105, (2004)
  • [9] Bao X., Zhang R., Apple grade identification method based on artificial neural network and image processing, Transactions of the CSAE, 20, 3, pp. 109-112, (2004)
  • [10] Xie Z., Zhang T., A new method of segmentation of strawberry image, Journal of China Agricultural University, 11, 1, pp. 84-86, (2006)
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