Machine vision application for food quality: A review

Research Journal of Applied Sciences, Engineering and Technology

Volumn 4, Issue 24, 2012, Pages 5453-5458

  Vijayarekha, K ^a  

^a SASTRA UNIVERSITY   (India)

Author keywords

Apples; Biscuits; Corn; Cucumber; Mushrooms; Nuts; Oranges; Potatoes; Rice; Strawberries; Whea

Indexed keywords

APPLES; BISCUITS; CORN; CUCUMBER; MUSHROOMS; NUTS; ORANGES; POTATOES; RICE; STRAWBERRIES; WHEA;

CAMERAS; CHARGE COUPLED DEVICES; FEATURE EXTRACTION; FOOD PRODUCTS; FRUITS; NONDESTRUCTIVE EXAMINATION; SPECTROSCOPY;

COMPUTER VISION;

EID: 84869778869     PISSN: 20407459     EISSN: 20407467     Source Type: Journal    
DOI: None     Document Type: Review

References (56)

1. Aleixos, N., J. Blasco, F. Navarron and E. Molto, 2002.Multispectral inspection of citrus in real time using machine vision and digital signal processors.Comput. Electr. Agric., 33(2002): 121-137.
2. Al-Mallahi, A., T. Kataoka and H. Okamoto, 2008.Discrimination between potato tubers and clods by detecting the significant wavebands. Bio. Syst.Eng., 100(2008): 329-337.
3. Al-Mallahi, A., T. Kataokab, H. Okamotob and Y.Shibata, 2010a. An image processing algorithm for detecting in-line potato tubers without singulation.Comput. Electr. Agric., 70(2010): 239-244.
4. Al-Mallahi, A., T. Kataoka, H. Okamoto and Y.Shibata, 2010b. Detection of potato tubers using an ultraviolet imaging-based machine vision system.Bio. Syst. Eng., 105(2010): 257-265.
5. Ann, P., S. Nico, T. Kathleen and M.N. Bart, 2002.Comparison of fourier transform and dispersive near infrared reflectance for apple quality measurements. Bio. Syst. Eng., 81(3): 305-311.
6. Barcelon, E.G., S. Tojo and K. Watanabe, 1999. Xray computed tomography for internal quality evaluation of peaches. J. Agric. Eng. Res., 73(4):323-330.
7. Blasco, J., N. Aleixosb, S. Cuberoa, J. Gómez-Sanchísa and E. Moltóa, 2009. Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Comput. Electr.Agric., 66(2009): 1-8.
8. Chandra, B.S., S.J. Digvir, P. Jitendra and D.G.W.Noel, 2010. Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Comput.Electr. Agric., 73(2010): 118-125.
9. Cheng-Jin, D. and W.S. Da, 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Techn., 15: 230-249.
10. Chenglong, W., L. Xiaoyu, W. Wei, F. Yaoze, Z. Zhu and Z. Hui, 2011. Recognition of worm-eaten chestnuts based on machine vision. Math. Comput. Modell., 54: 888-894.
11. Choudhary, R., S. Mahesh, J. Paliwal and D.S. Jayas,2009. Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Bio. Syst. Eng., 102: 115-127.
12. Clark, C.J., P.D. Hockings, D.C. Joyce and R.A.Mazucco, 1997. Application of magnetic resonance imaging to pre and post-harvest studies of fruits and vegetables. Postharvest Biol. Techn., 11(1):1-21.
13. Clark, C.J., V.A. McGlone and R.B. Jordan, 2003.Detection of Brownheart in 'Braeburn' apple by transmission NIR spectroscopy. Postharvest Biol.Techn., 28: 87-96.
14. Devrim, U. and G. Bernard, 2004. A quality grading approach for Janagold apples. Proceedings of SPS IEEE Benelux Signal Processing Symposium.
15. Diwan, P.A., L. Renfu and E.G. Daniel, 2006. Nearinfrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers.Comput. Electr. Agric., 53: 60-70.
16. Diwan, P.A. and L. Renfu, 2010. Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles. Comput.Electr. Agric., 74: 137-144.
17. Farhad, N. and S. Terry, 2002. Performance analysis and optimization of shape recognition and classification using ANN. Robot. Comput. Integ. Manuf., 18(3-4): 177-185.
18. Fito, P.J., M.D. Ortolá, R. De los Reyes, P. Fito and E. De los Reyes, 2004. Control of citrus surface drying by image analysis of infrared thermography.J. Food Eng., 61(3): 287-290.
19. Gunasekaran, S., 1996. Computer vision technology for food quality assurance. Trends Food Sci. Techn., 7(8): 245-256.
20. Gunasekaran, S., 2001. Non-Destructive Food Evaluation Techniques to Analyse Properties and Quality. Food Science and Technology Series, Marcel Dekker, New York, pp: 105.
21. Ishida, N., T. Kobayashi, M. Koizumi and H. Kano,1989. H-NMR imaging of tomato fruits. J. Agric. Biol. Chem., 53: 2363-2367.
22. Jesus, B., L.L.F. Ma., L. Eduard, V. Xavier, R. Inmaculada, O. Jorge, S. Guillermo and C. Xavier,2005. Evaluation of an electronic nose to assess fruit ripeness. IEEE Sens. J., 5(1).
23. Jiangbo, L., R. Xiuqin and Y. Yibin, 2011. Detection of common defects on oranges using hyperspectral reflectance imaging. Comput. Electr. Agric.,78(2011): 38-48.
24. Juan, X., B. Cedric, T.J. Pal, R. Herman and J. De Baerdemaeker, 2005. Detecting bruises on golden delicious apples using hyperspectral imaging with multiple wavebands. Bio. Syst. Eng., 90: 27-36.
25. Juan, X., S. Wouter and J. De Baerdemaeker, 2007.Combination of chemometric tools and image processing for bruise detection on apples. Comput.Electr. Agric., 56(2007): 1-13.
26. Karimi, Y., S.O. Prasher, R.M. Patel and S.H. Kim,2006. Application of support vector machine technology for weed and nitrogen stress detection in corn. Comput. Electr. Agric., 51: 99-109.
27. Leemans, V. and M.F. Destains, 2004. A real time grading method of apples based on features extracted from defects. J. Food Eng., 61: 83-89.
28. Li, Q. and M. Wang, 1999. Study on High-Speed apple surface defect segment algorithm based on computer vision. Proceedings of 99 International Conference on Agricultural Engineering.
29. Liu, H. and X. Wu, 2008. Rapid shelf-life identification model of citrus based on near infrared spectroscopy. IEEE International Symposium on Knowledge Acquisition and Modelling.
30. Mahmoud, O., M. Asghar and H.O. Mohammad, 2009.An intelligent system for sorting pistachio nut varieties. Exp. Syst. Appl., 36: 11528-11535.
31. Masoud, T., G. Aoife and P.O. Colm, 2009. Prediction of White button mushroom (Agaricus bisporus) moisture content using hyperspectral imaging.Sens. Instrumen. Food Qual., 3: 219-226.
32. Masoud, T., A.G. Aoife and P.O. Colm, 2011.Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Bio. Syst. Eng.108: 191-194.
33. Naoshi, K., A. Usman, M. Mitsuji and M. Haruhiko,2000. Machine vision based quality evaluation of Iyokan orange fruit using neural networks.Comput. Electr. Agric., 29: 135-147.
34. Nashat, S., A. Abdullah, S. Aramvith and M.Z.Abdullah, 2011. Support vector machine approach to real-time inspection of biscuits on moving conveyor belt. Comput. Electr. Agric., 75:147-158.
35. Li, Q., W. Maohua and G. Weikang, 2002. Computer vision based system for apple surface defect detection. Comput. Electr. Agric., 36: 215-223.
36. Rafael, C.G. and E.W. Richard, 2003. Digital Image Processing. 2nd Edn., Pearson Education, Upper Saddle River, New Jersey.
37. Seung, C.Y., P. Bosoon, C.L. Kurt, R.W. William and W.H. Gerald, 2011. Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta. Comput.Electr. Agric., 79: 159-168.
38. Thomas, P., A. Kannan, V.H. Degwekar and M.S.Ramamurthy, 1995. Non-destructive detection of seed weevil-infested mango fruits by X-ray imaging. Postharvest Biol. Techn., 5(1-2):161-165.
39. Tunde, V. and F. Jozsef, 2000. Enhancing colour differences in images of diseased mushrooms.Comput. Electr. Agric., 26: 187-198.
40. Upchurch B.L., J. A Throop, D.J. Aneshansley., 1997.Detecting internal breakdown in apples using interactance measurements. Postharvest Bio.Technol., 10: 15-19.
41. Veres, J.S., G.P. Cofer and G.A. Johnson, 1991.Distinguishing plant tissues with magnetic resonance microscopy. Am. J. Bot., 78: 1704-1711.
42. Velasco L.R.I. and Medina C.dr. 2004. Soft X-Ray imaging for non-destructive detection of mango pulp weevil (sternochetus frigidus fabr.) infestation in fresh mature green 'Carabao' Mango (Mangifera indica L.) Fruits. Philipp. Agric.87(2): 160-164.
43. Vijayarekha, K. and R. Govindaraj, 2004. Texture based citrus fruit external defect classification using neural network approach. Proceedings of International Conference.
44. Vijayarekha, K. and R. Govindaraj, 2005. Undecimated wavelet based citrus fruit external defect classification using neural network. Proceedings of the National Conference.
45. Vijayarekha, K. and R. Govindaraj, 2006. Citrus fruit external defect classification using wavelet packet transform features and ANN. Proceedings of IEEE International Conference on Industrial Technology,(ICIT 2006) Art. No. 4237968, pp: 2872-2877.
46. Vijayarekha, K., 2008. Multivariate image analysis for defect identification of apple fruit images. Proceedings of 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008,art. No. 4758175, pp: 1499-1503.
47. Wenbo, W. and P. Jitendra, 2007. Near-infrared spectroscopy and Imaging in food quality and safety. Sens. Instrumen. Food Qual., 1: 193-207, Springer Science + Business Media, LLC 2007.
48. Xiao-boa, Z., Z. Jie-wena, Y. Li and H. Mel, 2010. Inline detection of apple defects using three color cameras system. Comput. Electr. Agric., 70:129-134.
49. Xiao, C., X. Yi, L. Wei and Z. Junxiong, 2010.Combining discriminant analysis and neural networks for corn variety identification. Comput.Electr. Agric. 71S(2010): S48-S53.
50. Xing, J., P. Jancso and J. De Baerdemaeker, 2007.Stem-end/Calyx identification on apples using contour analysis in multispectral images. Biosyst.Eng., 96(2): 231-237.
51. Xu, L. and Y. Zhao, 2010. Automated strawberry grading system based on image processing.Comput. Electr. Agric., 71: 32-39.
52. Yadav, B.K. and V.K. Jindal, 1998. Monitoring Milled Rice Characteristics by Image Analysis. In:Salokhe, V.M. and Z. Jianxia (Eds.), Proceedings of the International Agricultural Engineering Conference, Bangkok, Thailand, 7-10 December,pp: 963-971.
53. Yasmin, Y., A. Hasnah, S. Puteh, A.A.R. Rafikha and I.Sabarina, 2005. Proceedings of the International Conference on 22nd-24th November 2005, Information Technology and Multimedia at UNITEN (ICIMU '05), Malaysia.
54. Yud-Ren, C., C. Kuanglin and S.K. Moon, 2002.Machine vision technology for agricultural applications. Comput. Electr. Agric., 36: 173-191.
55. Zhiqing, W. and T. Yang, 1990. Building a rule based machine vision system for defect inspection on apple sorting and packing lines. Exp. Syst. Appl.,16: 307-313.
56. Zion, B., P. Chen and M.J. McCarthy, 1995. Detection of bruises in magnetic resonance images of apples.J. Comput. Electr. Agric., (13): 289-299.