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Mechatronic components in apple sorting machines with computer vision

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Abstract

A mechatronic system is a special application of mechanical engineering with a more focus on automation using emerging technologies such as computer sciences, electronics, and control instead of entirely mechanical elements. In recent years, mechatronics plays an important role in automated food industry. Apple is one of horticultural products that contain valuable nutrient. External appearance of apple is one of the important factors affecting the market price and customer satisfaction. By now, various machines have been developed for automated sorting of fruit and vegetables but still major grading and sorting operations are manually preformed in the packing houses. Computer vision provides an automated, non-destructive, cost-effective and objective technique to satisfy these needs. In this study, various components of the computer vision inspection systems for quality evaluation of apple are reviewed. Recently, infrared (IR) imaging, multispectral imaging (MSI) and hyperspectral imaging (HSI) systems are also being used as advanced techniques, which could acquire high quality spatial and spectral information for online quality and safety inspection of agricultural products. Therefore, the current article includes various research conducted in these domains as well. Finally, different pattern recognition techniques for apple defect detection and the stem-calyx region identification are discussed.

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References

  1. N. Aleixos, J. Blasco, F. Navarron, E. Molto, Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput. Electron. Agric. 33(2), 121–137 (2002)

    Article  Google Scholar 

  2. S.H. Deck, C.T. Morrow, P.H. Heinemann, H.J. Sommer, Comparison of a neural network and traditional classifier for machine vision inspection of potatoes. Appl. Eng. Agric. 11(2), 319–326 (1995)

    Article  Google Scholar 

  3. P.M. Bato, M. Nagata, Q. Cao, K. Hiyoshi, T. Kitahara, Study on sorting system for strawberry using machine vision (Part 2). J. Jpn. Soc. Agric. Mach. 62(2), 101–110 (2000)

    Google Scholar 

  4. S. Gunasekaran, Computer vision technology for food quality assurance. Trends Food Sci Technol. 7, 245–256 (1996)

    Article  CAS  Google Scholar 

  5. R.M. Haralick, L.C. Shapiro, Computer and Robot Vision. (Addison-Wesley Publishing Company, Reading, MA, 1992)

    Google Scholar 

  6. E. Cubero, Diseño e Implementación de Nuevas Tecnologías Basadas en Visión Artificial para la Inspección no Destructiva de la Calidad de Fruta en Campo y Mínimamente Procesada. (212p). Tiesis (Tesis Doctoral)-Departamento de Expresión Gráfica Arquitectónica, Universidad Politécnica de Valencia, (2012)

  7. N.R. Sarkar, Machine vision for quality control in the food industry, in Instrumental Methods for Quality Assurance in Foods, ed. by I. Fung, E. Matthews (ASQC Quality Press, Marcel Dekker Inc., New York, 1991), pp. 166–188

    Google Scholar 

  8. A. Novini, The latest in vision technology in today’s food and beverage container manufacturing industry. in Food Processing Automation IV Proceedings of the 1995 Conference. ASAE, St. Joseph, MI (1995)

    Google Scholar 

  9. D. Unay, B. Gosselin, Automatic defect segmentation of ‘Jonagold’ apples on multi-spectral images: a comparative study. Postharvest Biol. Technol. 42(3), 271–279 (2006)

    Article  Google Scholar 

  10. X. Cheng, Y. Tao, Y.R. Chen, Y. Luo, NIR- MIR dual-sensor machine vision system for online apple stemend/calyx recognition. Trans. ASAE. 46(2), 551–558 (2003)

    Article  Google Scholar 

  11. T. Susnjak, A. Barczak, N. Reyes, A decomposition machine-learning strategy for automated fruit grading. in Proceedings of the World Congress on Engineering and Computer Science, Vol II. WCECS, San Francisco (2013)

    Google Scholar 

  12. V. Leemans, M.F. Destain, A real time grading method of apples based on features extracted from defects. J. Food Eng. 61, 83–89 (2004)

    Article  Google Scholar 

  13. Z. Xiaobo, Z. Jiewen, L. Yanxiao, Apple colour grading based on organization feature parameters. Pattern Recogn. Lett. 28(15), 2046–2053 (2007)

    Article  Google Scholar 

  14. D. Zhang, K.D. Lillywhite, D.J. Lee, B.J. Tippetts, Automated apple stem end and calyx detection using evolution-constructed Features. J. Food Eng. 119(3), 411–418 (2013)

    Article  Google Scholar 

  15. Q. Li, M. Wang, W. Gu, Computer vision based system for apple surface defect detection. Comput. Electron. Agric. 36(2–3), 215–223 (2002)

    Article  Google Scholar 

  16. Z. Xiaobo, Z. Jie-wen, L. Yanxiao, M. Holmes, In-line detection of apple defects using three color cameras system. Comput. Electron. Agric. 70(1), 129–134 (2010)

    Article  Google Scholar 

  17. V.D. Sadegaonkar, K.H. Wagh, Automatic sorting using computer vision and image processing for improving apple quality. Int. J. Innovative Res. Dev. 4(1), 11–14 (2015)

    Google Scholar 

  18. A.N. Lorestani, M. Omid, A. Bagheri Shooraki, A.M. Borghei, A. Tabatabaeefar, Design and evaluation of a fuzzy logic based decision support system for grading of golden delicious apples. Int. J. Agric. Biol. 8(4), 440–444 (2006)

    Google Scholar 

  19. K.A. Vakilian, J. Massah, An apple grading system according to european fruit quality standard using Gabor filter and artificial neural networks. Sci. Study Res. Chem. Chem. Eng. Biotechnol. Food Ind. 17(1), 75–85 (2016)

    Google Scholar 

  20. M.M. Sofu, O. ER, M.C. Kayacan, B. Cetisli, Design of an automatic apple sorting system using machine vision. Comput. Electron. Agric. 127, 395–405 (2016)

    Article  Google Scholar 

  21. B.S. Bennedsen, D.L. Peterson, A. Tabb, Identifying defects in images of rotating apples. Comput. Electron. Agric. 48(2), 92–102 (2005)

    Article  Google Scholar 

  22. J.A. Throop, D.J. Aneshansley, W.C. Anger, Inspection station detects defects on apples in real time. ASAE Annual Meeting. Paper No. 993205, (1999)

  23. J.A. Throop, D.J. Aneshansley, W.C. Anger, D.L. Peterson, Quality evaluation of apples based on surface defects—an inspection station design. ASAE Annual Meeting. Paper No. 036161, (2003)

  24. J.A. Throop, D.J. Aneshansley, W.C. Anger, D.L. Peterson, Quality evaluation of apples based on surface defects: development of an automated inspection system. Postharvest Biol. Technol. 36(3), 281–290 (2005)

    Article  Google Scholar 

  25. J. Blasco, N. Aleixos, E. Moltio, Machine vision system for automatic quality grading of fruit. Biosys. Eng. 85(4), 415–423 (2003)

    Article  Google Scholar 

  26. Y.C. Chio, C.H. Chen, Development of online apple bruise detection system. Eng. Agric. Environ. Food (2017). https://doi.org/10.1016/j.eaef.2017.03.003

    Google Scholar 

  27. D. Unay, B. Gosselin, O. Kleynen, V. Leemans, M.F. Destain, O. Debeir, Automatic grading of bi-colored apples by multispectral machine vision. Comput. Electron. Agric. 75(1), 204–212 (2011)

    Article  Google Scholar 

  28. D. Unay, B. Gosselin, O. Debeir, Apple stem and calyx recognition by decision trees. Proceedings of the 6th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP, pp 549–552, (2006)

  29. J. Xing, W. Saeys, J.D. Baerdemaeker, Combination of chemometric tools and image processing for bruise detection on apples. Comput. Electron. Agric. 56(1), 1–13 (2007)

    Article  Google Scholar 

  30. J.C. Keresztes, M. Goodarzi, W. Saeys, Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction. Food Control. (2016). https://doi.org/10.1016/j.foodcont.2016.02.007

    Google Scholar 

  31. R. Lu, Detection of bruises on apples using near-infrared hyperspectral imaging. Trans. ASAE. 46(2), 523–530 (2003)

    Google Scholar 

  32. B. Zhang, Sh Fan, J. Li, W. Huang, Ch Zhao, M. Qian, L. Zheng, Detection of early rottenness on apples by using hyperspectral imaging combined with spectral analysis and image processing. Food Anal. Methods. (2015). https://doi.org/10.1007/s12161-015-0097-7

    Google Scholar 

  33. J.A. Throop, D.J. Aneshansley, Apple damage segmentation utilizing reflectance spectra of the defect. ASAE Annual Meeting Paper No. 97-3078, p. 17, (1997)

  34. I. Kavdir, D.E. Guyer, Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosys. Eng. 89(3), 331–334 (2004)

    Article  Google Scholar 

  35. B. Zhu, L. Jiang, Y. Luo, Y. Tao, Gabor feature-based apple quality inspection using kernel principal component analysis. J. Food Eng. 81(4), 741–749 (2007)

    Article  Google Scholar 

  36. V. Leemans, H. Magein, M.F. Destain, Defect segmentation on Jonagold apples using colour vision and a Bayesian classification method. Comput. Electron. Agric. 23, 43–53 (1999)

    Article  Google Scholar 

  37. V. Leemans, H. Magein, M.F. Destain, Defects segmentation on Golden Delicious apples by using colour machine vision. Comput. Electron. Agric. 20(2), 117–130 (1998)

    Article  Google Scholar 

  38. V. Leemans, H. Magein, M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosys. Eng. 83(4), 397–404 (2002)

    Article  Google Scholar 

  39. G. Elmasry, N. Wang, C. Vigneault, Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biol. Technol. 52(1), 1–8 (2009)

    Article  Google Scholar 

  40. I. Kavdir, D.E. Guyer, Apple sorting using artificial neural networks and special imaging. Trans. ASAE. 45(6), 1995–2005 (2002)

    Article  Google Scholar 

  41. K. Nakano, Application of neural networks to the color grading of apples. Comput. Electron. Agric. 18(2–3), 105–116 (1997)

    Article  Google Scholar 

  42. H. Toylan, H. Kuscu, A real-time apple grading system using multicolour space. Hindawi Publishing Corporation. (2014). https://doi.org/10.1155/2014/292681

    Google Scholar 

  43. R. Mizushima, Lu, An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method. Comput. Electron. Agric. 94, 29–37 (2013)

    Article  Google Scholar 

  44. P. Baranowski, W. Mazurek, J. Wozniak, U. Majewska, Detection of early bruises in apples using hyperspectral data and thermal imaging. J. Food Eng. 110(3), 345–355 (2012)

    Article  Google Scholar 

  45. O. Kleynen, V. Leemans, M.F. Destain, Development of a multi-spectral vision system for the detection of defects on apples. J. Food Eng. 69(1), 41–49 (2005)

    Article  Google Scholar 

  46. P.M. Mehl, Y.R. Chen, M.S. Kim, D.E. Chan, Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 61(1), 67–81 (2004)

    Article  Google Scholar 

  47. C. Puchalski, J. Gorzelany, G. Zaguła, G. Brusewitz, Image analysis for apple defect detection. Biosyst. Agric. Eng. 8, 197–205 (2008)

    Google Scholar 

  48. B. Zhang, W. Huang, L. Gong, J. Li, C. Zhao, C. Liu, H. Danfeng, Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. J. Food Eng. 146, 143–151 (2015)

    Article  Google Scholar 

  49. D.W. Penman, Determination of stem and calyx location on apples using automatic visual inspection. Comput. Electron. Agric. 33(1), 7–18 (2001)

    Article  Google Scholar 

  50. F. Mendoza, R. Lu, H. Cen, Grading of apples based on firmness and soluble solids content using Vis/SWNIR spectroscopy and spectral scattering techniques. J. Food Eng. 125, 59–68 (2014)

    Article  CAS  Google Scholar 

  51. M. Suresha, N.A. Shilpa, B. Soumya, Apples grading based on SVM classifier. IJCA Proceedings on National Conference on Advanced Computing and Communications, (2012), pp. 27–30

  52. Z. Wen, Y. Tao, Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines. Expert Syst. Appl. 16(3), 307–313 (1999)

    Article  Google Scholar 

  53. S.H. Mohana, C.J. Prabhakar, P.U. Praveen, Kumar, Surface defect detection and grading of apples. Assoc. Comput. Electron. Electr. Eng. 57–64 (2013)

  54. I. Kavdir, D.E. Guyer, Evaluation of different pattern recognition techniques for apple sorting. Biosys. Eng. 99(2), 211–219 (2008)

    Article  Google Scholar 

  55. Q. Li, M. Wang, Study on high-speed apple surface defect segment algorithm based on computer vision. in Proceedings of International Conference on Agricultural Engineering (pp. 27–31). Beijing, China, (1999)

  56. D. Shive Rame, J. Anand, Singh, Detection and classification of apple fruit diseases using complete local binary patterns. in Third International Conference on Computer and Communication Technology, (2012) https://doi.org/10.1109/ICCCT.2012.76

  57. J. Kafashan, N. Bagheri, Applications of mechatronics engineering in the agro-industrial development (Challenges and Solutions). Q. J. Ind. Technol. Dev. Electron. Version 16(31), 55–68 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Biosystems Engineering, University of Tabriz, Tabriz, Iran

    Nesar Mohammadi Baneh & Hosein Navid

  2. Department of Mechanical Engineering in Agro Machinery & Mechanization, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran

    Jalal Kafashan

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  1. Nesar Mohammadi Baneh
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  2. Hosein Navid
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  3. Jalal Kafashan
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Correspondence to Nesar Mohammadi Baneh or Hosein Navid.

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Mohammadi Baneh, N., Navid, H. & Kafashan, J. Mechatronic components in apple sorting machines with computer vision. Food Measure 12, 1135–1155 (2018). https://doi.org/10.1007/s11694-018-9728-1

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  • DOI: https://doi.org/10.1007/s11694-018-9728-1

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