Detection of Pecan Quality Based on Multi-feature Fusion and Level Set

被引：0
|
作者：
Liu Z. [1 ]
Zou X. [2 ]
Song Y. [1 ]
Wang M. [1 ]
Su J. [1 ]
机构：
[1] School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang
[2] School of Food and Biological Engineering, Jiangsu University, Zhenjiang
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷 / 12期
关键词：
Level set; Multi-feature; Non-destructive test; Pecan; Support vector machine;
D O I：
10.6041/j.issn.1000-1298.2019.12.040
中图分类号：
学科分类号：
摘要：
Pecan is one of the top ten nuts in the world. Because of its good taste and rich nutrition, it is loved by people. But pecan is easy to deteriorate in the process of production and processing. Mistaken food can cause many hazards to human body. To solve this problem, a method for detecting the quality of pecans was proposed based on multi-feature fusion and level set. Taking thin-shelled pecans as research object, and the original image was preprocessed to solve the problem that the target object did not match the background area. The adaptive DRLSE method with improved edge indication function was used to segment the pecans in the image, and the statistical features of the gray histogram of the image were extracted. Multi-features such as co-occurrence matrix, Tamura and local binary mode were combined and analyzed. The SVM discriminant model was established to realize the non-destructive quality detection of pecans. The experiment collected 200 normal, rancid pecans sample images, and subjected to image rancidity and multi-feature analysis. The experimental results showed that the adaptive DRLSE segmentation method with improved edge indication function can complete the segmentation better than the traditional method even inside or outside the target. The accuracy of the method was as high as 96.15% in judging whether pecan was rancid or not, and on this basis, the average recognition rate was 90.81% in judging the degree of pecan rancidity. © 2019, Chinese Society of Agricultural Machinery. All right reserved.
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页码：348 / 356and364
相关论文
共 21 条
  • [1] Chen M., Study on the comparison of main nutrient components of pecan and its processing effects, (2017)
  • [2] Zhi H., Han Y., Acidity and inhibition of walnut kernels, Packaging Engineering, 27, 5, pp. 22-24, (2006)
  • [3] Kass M., Witkin A., Terzopoulos D., Snakes: active contour models, International Journal of Computer Vision, 1, 4, pp. 321-331, (1988)
  • [4] Geng N., Yu W., Ning J., Agricultural image segmentation method based on level set and prior information, Transactions of the Chinese Society for Agricultural Machinery, 42, 9, pp. 167-172, (2011)
  • [5] Quispe S.C., Tapia J.D.B., Paredes M.N.L., Et al., Automation of the Brazil-nuts classification process using dynamic level set, 11th International Conference on Hybrid Intelligent Systems, HIS 2011, (2011)
  • [6] Qi M., Wang C., Jiang J., Et al., Pedestrian recognition by multi-feature fusion and alternating direction multiplier method, Chinese Journal of Image Graphics, 23, 6, pp. 827-836, (2018)
  • [7] Hu Y., Song L., Zhang J., Et al., Multi-feature fusion pest image recognition based on sparse representation, Pattern Recognition and Artificial Intelligence, 27, 11, pp. 985-992, (2014)
  • [8] Zhang C., Wang R., Xie C., Automatic classification method of pest image based on multi-feature dictionary learning, Computer Application and Software, 3, pp. 142-147, (2017)
  • [9] Zhang Y., Jiang M., Yu P., Et al., Agricultural pest image recognition method based on multi-feature fusion and sparse representation, Scientia Agricultura Sinica, 51, 11, pp. 67-76, (2018)
  • [10] Feng X., Ten-year review of the development of MRI technology, Progress in Biomedical Engineering, 27, 2, pp. 119-123, (2006)
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