Advertisement

Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest

  • José Blasco
  • Sandra Munera
  • Nuria Aleixos
  • Sergio Cubero
  • Enrique MoltoEmail author
Chapter
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 161)

Abstract

Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product’s surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

Keywords

Hyperspectral Image processing In-line inspection Postharvest Quality Real-time Spectral imaging 

Notes

Acknowledgement

This work has been partially funded by INIA through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds.

References

  1. 1.
    Cubero S, Lee WS, Aleixos N, Albert F, Blasco J (2016) Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest: a review. Food Bioproc Tech 9:1623–1639Google Scholar
  2. 2.
    Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J, Blasco J (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioproc Tech 4:487–504CrossRefGoogle Scholar
  3. 3.
    Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioproc Tech 5:1121–1142CrossRefGoogle Scholar
  4. 4.
    Hunt RWG, Pointer MR (2011) Measuring colour, 4th edn. Wiley, ChichesterCrossRefGoogle Scholar
  5. 5.
    Sharma RM, Singh RR (2000) Harvesting, postharvest, handling and physiology of fruits and vegetables. In: Verma LR, Joshi VK (eds) Postharvest technology of fruit and vegetables. Indus Publishing Co. New Delhi, pp 94–147Google Scholar
  6. 6.
    Mohammadi V, Kheiralipour K, Ghasemi-Varnamkhasti M (2015) Detecting maturity of persimmon fruit based on image processing technique. Sci Hortic 184:123–128CrossRefGoogle Scholar
  7. 7.
    Taghadomi-Saberi S, Omid M, Emam-Djomeh Z, Faraji-Mahyari KH (2015) Determination of cherry color parameters during ripening by artificial neural network assisted image processing technique. J Agric Sci Technol 17:589–600Google Scholar
  8. 8.
    Baltazar A, Aranda JI, González-Aguilar G (2008) Bayesian classification of ripening stages of tomato fruit using acoustic impact and colorimeter sensor data. Comput Electron Agric 60:113–121CrossRefGoogle Scholar
  9. 9.
    El-Bendary N, El Hariri E, Hassanien AE, Badr A (2015) Using machine learning techniques for evaluating tomato ripeness. Expert Syst Appl 42:1892–1905CrossRefGoogle Scholar
  10. 10.
    Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, Blasco J (2013) Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food Bioproc Tech 6(2):530–541CrossRefGoogle Scholar
  11. 11.
    Guzmán E, Baeten V, Pierna JAF, García-Mesa JA (2015) Determination of the olive maturity index of intact fruits using image analysis. J Food Sci Technol 52:1462–1470CrossRefGoogle Scholar
  12. 12.
    Vidal A, Talens P, Prats-Montalbán JM, Cubero S, Albert F, Blasco J (2013) In-line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food Bioproc Tech 6(12):3412–3419CrossRefGoogle Scholar
  13. 13.
    Surya Prabha D, Satheesh Kumar J (2015) Assessment of banana fruit maturity by image processing technique. J Food Sci Technol 52:1316–1327CrossRefGoogle Scholar
  14. 14.
    Vélez-Rivera N, Blasco J, Chanona-Pérez JJ, Calderón-Domínguez G, Perea-Flores MJ, Arzate-Vázquez I, Cubero S, Farrera-Rebollo R (2014) Computer vision system applied to classification of ‘Manila’ mangoes during ripening process. Food Bioproc Tech 7:1183–1194CrossRefGoogle Scholar
  15. 15.
    Li JB, Huang WQ, Zhao CJ (2015) Machine vision technology for detecting the external defects of fruits: a review. Imaging Sci J 63:241–251CrossRefGoogle Scholar
  16. 16.
    Blasco J, Aleixos N, Moltó E (2007) Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. J Food Eng 81:535–543CrossRefGoogle Scholar
  17. 17.
    Al-Rahbi S, Manickavasagan A, Al-Yahyai R, Khriji L, Alahakoon P (2013) Detecting surface cracks on dates using color imaging technique. Food Sci Technol Res 19:795–804CrossRefGoogle Scholar
  18. 18.
    Blasco J, Aleixos N, Gómez J, Moltó E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83:384–393CrossRefGoogle Scholar
  19. 19.
    Blasco J, Aleixos N, Gómez-Sanchis J, Moltó E (2009) Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosyst Eng 103:137–145CrossRefGoogle Scholar
  20. 20.
    Li J, Rao X, Wang F, Wu W, Ying Y (2013) Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biol Technol 82:59–69CrossRefGoogle Scholar
  21. 21.
    Rokunuzzaman M, Jayasuriya HPW (2013) Development of a low cost machine vision system for sorting of tomatoes. Agric Eng Int CIGR J 15:173–180Google Scholar
  22. 22.
    Xu L, You Z, Wu S, Zhao H, Wu L (2013) Development and experiment on automatic grading equipment for kiwi INMATEH - agricultural. Engineering 41:55–64Google Scholar
  23. 23.
    Aleixos N, Blasco J, Navarrón F, Moltó E (2002) Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput Electron Agric 33:121–137CrossRefGoogle Scholar
  24. 24.
    Unay D, Gosselin B, Kleynen O, Leemans V, Destain MF, Debeir O (2011) Automatic grading of bi-colored apples by multispectral machine vision. Comput Electron Agric 75:204–212CrossRefGoogle Scholar
  25. 25.
    Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C (2010) Sensing technologies for precision specialty crop production. Comput Electron Agric 74:2–33CrossRefGoogle Scholar
  26. 26.
    Pourreza A, Lee WS, Ehsani R, Schueller JK, Raveh E (2015) An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Comput Electron Agric 110:221–232CrossRefGoogle Scholar
  27. 27.
    Pourreza A, Lee WS, Etxeberria E, Banerjee A (2015) An evaluation of a vision-based sensor performance in Huanglongbing disease identification. Biosyst Eng 130:13–22CrossRefGoogle Scholar
  28. 28.
    Sun D-W (ed) (2010) Hyperspectral imaging for food quality analysis and control. Academic Press, Elsevier Science, London, EnglandGoogle Scholar
  29. 29.
    Gat N (2000) Imaging spectroscopy using tunable filters: a review. Proc SPIE 4056:50–64CrossRefGoogle Scholar
  30. 30.
    Gómez-Sanchis J, Gómez-Chova L, Aleixos N, Camps-Valls G, Montesinos-Herrero C, Moltó E, Blasco J (2008) Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J Food Eng 89:80–86CrossRefGoogle Scholar
  31. 31.
    Gómez-Sanchis J, Martín-Guerrero JD, Soria-Olivas E, Martínez-Sober M, Magdalena-Benedito R, Blasco J (2012) Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Syst Appl 39:780–785CrossRefGoogle Scholar
  32. 32.
    Lorente D, Blasco J, Serrano AJ, Soria-Olivas E, Aleixos N, Gómez-Sanchis J (2013) Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food Bioproc Tech 6:3613–3619CrossRefGoogle Scholar
  33. 33.
    Brandily ML, Monbet V, Bureau B, Boussard-Plédel C, Loréal O, Adam JL, Sire O (2011) Identification of foodborne pathogens within food matrices by IR spectroscopy. Sens Actuators B 160:202–206CrossRefGoogle Scholar
  34. 34.
    Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46:99–118CrossRefGoogle Scholar
  35. 35.
    Zhao J, Ouyang Q, Chen Q, Wang J (2010) Detection of bruise on pear by hyperspectral imaging sensor with different classification algorithms. Sens Lett 8:570–576CrossRefGoogle Scholar
  36. 36.
    Bei L, Dennis GI, Miller HM, Spaine TW, Carnahan JW (2004) Acousto-optic tunable filters: fundamentals and applications as applied to chemical analysis techniques. Prog Quantum Electron 28:67–87CrossRefGoogle Scholar
  37. 37.
    Jiménez A, Beltrán G, Aguilera MP, Uceda M (2008) A sensor-software based on artificial neural network for the optimization of olive oil elaboration process. Sens Actuators B 129:985–990CrossRefGoogle Scholar
  38. 38.
    Vila-Francés J, Calpe-Maravilla J, Gómez-Chova L, Amorós-López J (2010) Analysis of acousto-optic tunable filter performance for imaging applications. Opt Eng 49:113203–113209CrossRefGoogle Scholar
  39. 39.
    Wang W, Li C, Tollner EW, Rains GC, Gitaitis RD (2012) A liquid crystal tunable filter based shortwave infrared spectral imaging system: design and integration. Comput Electron Agric 80:126–134CrossRefGoogle Scholar
  40. 40.
    Gowen AA, O'Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18:590–598CrossRefGoogle Scholar
  41. 41.
    Vila-Francés J, Calpe-Maravilla J, Gómez-Chova L, Amorós-López J (2011) Design of a configurable multispectral imaging system based on an AOTF. IEEE Trans Ultrason Ferroelectr Freq Control 58:259–262CrossRefGoogle Scholar
  42. 42.
    Hecht E (2003) Optics, 4th edn. Addison Wesley, ReadingGoogle Scholar
  43. 43.
    Geladi PLM (2007) Calibration standards and image calibration. In: Grahn HF, Geladi P (eds) Techniques and applications of hyperspectral image analysis. Wiley, Chichester, pp 203–220CrossRefGoogle Scholar
  44. 44.
    Schmilovitch Z, Ignat T, Alchanatis V, Gatker J, Ostrovsky V, Felföldi J (2014) Hyperspectral imaging of intact bell peppers. Biosyst Eng 117:83–93CrossRefGoogle Scholar
  45. 45.
    Rajkumar P, Wang N, ElMasry G, Raghavan GSV, Gariepy Y (2012) Studies on banana fruit quality and maturity stages using hyperspectral imaging. J Food Eng 108:194–200CrossRefGoogle Scholar
  46. 46.
    Munera S, Besada C, Blasco J, Cubero S, Salvador A, Talens P, Aleixos N (2017) Astringency assessment of persimmon by hyperspectral imaging. Postharvest Biol Technol 125:35–4Google Scholar
  47. 47.
    Gaston E, Frías JM, Cullen PJ, O’Donnell CP, Gowen AA (2010) Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem 58:6226–6233Google Scholar
  48. 48.
    Yang YC, Sun DW, Pu H, Wang NN, Zhu Z (2015) Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion. Postharvest Biol Technol 103:55–65CrossRefGoogle Scholar
  49. 49.
    Hua MH, Dong QL, Liu BL, Opara UL, Chen L (2015) Estimating blueberry mechanical properties based on random frog selected hyperspectral data. Postharvest Biol Technol 106:1–10CrossRefGoogle Scholar
  50. 50.
    Leiva-Valenzuela GA, Lu R, Aguilera JM (2013) Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J Food Eng 115:91–98CrossRefGoogle Scholar
  51. 51.
    Leiva-Valenzuela GA, Lu R, Aguilera JM (2014) Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Innov Food Sci Emerg Technol 24:2–13Google Scholar
  52. 52.
    Liu C, Liu W, Chen W, Yang J, Zheng L (2015) Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit. Food Chem 173:482–488CrossRefGoogle Scholar
  53. 53.
    Nogales-Bueno J, Hernández-Hierro JM, Rodríguez-Pulido FJ, Heredia FJ (2014) Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: a preliminary approach. Food Chem 152:586–591CrossRefGoogle Scholar
  54. 54.
    Nogales-Bueno J, Rodríguez-Pulido FJ, Heredia FJ, Hernández-Hierro JM (2015) Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from La Rioja (Spain). Talanta 131:412–416CrossRefGoogle Scholar
  55. 55.
    Chen S, Zhang F, Ning J, Liu X, Zhang Z, Yang S (2015) Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem 172:788–793CrossRefGoogle Scholar
  56. 56.
    Baiano A, Terracone C, Peri G, Romaniello R (2012) Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Comput Electron Agric 87:142–151CrossRefGoogle Scholar
  57. 57.
    Lü Q, Tang M, Cai J, Zhao J, Vittayapadung S (2011) Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits. Czech J Food Sci 29:595–602Google Scholar
  58. 58.
    Baranowski P, Mazurek W, Pastuszka-Wozniak J (2013) Supervised classification of bruised apples with respect to the time. Postharvest Biol Technol 86:249–258Google Scholar
  59. 59.
    Vélez-Rivera N, Gómez-Sanchis J, Chanona-Pérez J, Carrasco JJ, Millán-Giraldo M, Lorente D, Cubero S, Blasco J (2014) Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst Eng 122:91–98CrossRefGoogle Scholar
  60. 60.
    Lee WH, Kim MS, Lee H, Delwiche SR, Bae H, Kim DY, Cho BK (2014) Hyperspectral near-infrared imaging for the detection of physical damages of pear. J Food Eng 130:1–7CrossRefGoogle Scholar
  61. 61.
    Cho BK, Kim MS, Baek IS, Kim DY, Lee WH, Kim J, Bae H, Kim YS (2013) Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery. Postharvest Biol Technol 76:40–49CrossRefGoogle Scholar
  62. 62.
    Yu K, Zhao Y, Li X, Shao Y, Zhu F, He Y (2014) Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing. Comput Electron Agric 103:1–10CrossRefGoogle Scholar
  63. 63.
    Haffa RP, Saranwongb S, Thanapase W, Janhiran A, Kasemsumran S, Kawano S (2013) Automatic image analysis and spot classification for detection of fruitfly infestation in hyperspectral images of mangoes. Postharvest Biol Technol 86:23–28CrossRefGoogle Scholar
  64. 64.
    Wang J, Nakano K, Ohashi S, Kubota Y, Takizawa K, Sasaki Y (2011) Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging. Biosyst Eng 108:345–351CrossRefGoogle Scholar
  65. 65.
    Gómez-Sanchis J, Blasco J, Soria-Olivas E, Lorente D, Escandell-Montero P, Martínez-Martínez JM, Martínez-Sober M, Aleixos N (2013) Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biol Technol 82:76–86CrossRefGoogle Scholar
  66. 66.
    Gómez-Sanchis J, Lorente D, Soria-Olivas E, Aleixos N, Cubero S, Blasco J (2014) Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food Bioproc Tech 7:1047–1056CrossRefGoogle Scholar
  67. 67.
    Simko I, Jimenez-Berni JA, Furbank RT (2015) Detection of decay in fresh-cut lettuce using hyperspectral imaging. Postharvest Biol Technol 106:44–52CrossRefGoogle Scholar
  68. 68.
    Qin J, Burks TF, Ritenour MA, Gordon Bonn W (2009) Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J Food Eng 93:183–191CrossRefGoogle Scholar
  69. 69.
    Zhao X, Burks TF, Qin J, Ritenour MA (2010) Effect of fruit harvest time on citrus canker detection using hyperspectral reflectance imaging. Sens Instrum Food Qual Saf 4:126–135CrossRefGoogle Scholar
  70. 70.
    Qin J, Burks TF, Zhao X, Niphadkar N, Ritenour MA (2011) Multispectral detection of citrus canker using hyperspectral band selection. Trans ASABE 54:2331–2341CrossRefGoogle Scholar
  71. 71.
    Qin J, Burks TF, Zhao X, Niphadkar N, Ritenour MA (2012) Development of a two-band spectral imaging system for real-time citrus canker detection. J Food Eng 108:87–93CrossRefGoogle Scholar
  72. 72.
    Wang W, Li C, Tollner EW, Gitaitis RD, Rains GC (2012) Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia) infected onions. J Food Eng 109:36–48Google Scholar
  73. 73.
    Everard CD, Kim MS, Lee H (2014) A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves. J Food Eng 143:139–145CrossRefGoogle Scholar
  74. 74.
    Yang CC, Kim MS, Millner P, Chao K, Cho B-K, Mo C, Lee H, Chan DE (2014) Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Postharvest Biol Technol 93:1–8Google Scholar
  75. 75.
    Yang CC, Kim MS, Kang S, Cho BK, Chao K, Lefcourt AM, Chan DE (2012) Red to far-red multispectral fluorescence image fusion for detection of fecal contamination on apples. J Food Eng 108:312–319Google Scholar
  76. 76.
    Teena MA, Manickavasagan A, Ravikanth L, Jayas DS (2014) Near infrared (NIR) hyperspectral imaging to classify fungal infected date fruits. J Stored Prod Res 59:306–313CrossRefGoogle Scholar
  77. 77.
    Al-Mallahi A, Kataoka T, Okamoto H, Shibata Y (2010) Detection of potato tubers using an ultraviolet imaging-based machine vision system. Biosyst Eng 105:257–265CrossRefGoogle Scholar
  78. 78.
    ElMasry G, Cubero S, Moltó E, Blasco J (2012) In-line sorting of irregular potatoes by using automated computer-based machine vision system. J Food Eng 112:60–68CrossRefGoogle Scholar
  79. 79.
    Kohno Y, Kondo N, Iida M, Kurita M, Shiigi T, Ogawa Y, Kaichi T, Okamoto S (2011) Development of a mobile grading machine for citrus fruit. Eng Agric Environ Food 4:7–11CrossRefGoogle Scholar
  80. 80.
    Cubero S, Aleixos N, Albert A, Torregrosa A, Ortiz C, García-Navarrete O, Blasco J (2014) Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precis Agric 15:80–94CrossRefGoogle Scholar
  81. 81.
    Leemans V, Destain M-F (2004) A real-time grading method of apples based on features extracted from defects. J Food Eng 6:83–89CrossRefGoogle Scholar
  82. 82.
    Bennedsen BS, Peterson DL, Tabb A (2005) Identifying defects in images of rotating apples. Comput Electron Agric 48:92–102CrossRefGoogle Scholar
  83. 83.
    Xiao-bo Z, Jie-wen Z, Yanxiao L, Holmes M (2010) In-line detection of apple defects using three color cameras system. Comput Electron Agric 70:129–134CrossRefGoogle Scholar
  84. 84.
    Reese D, Lefcourt AM, Kim MS, Lo YM (2010) Using parabolic mirrors for complete imaging of apple surfaces. Bioresour Technol 100:4499–4506CrossRefGoogle Scholar
  85. 85.
    Blasco J, Aleixos N, Cubero S, Gómez-Sanchis J, Moltó E (2009) Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Comput Electron Agric 66:1–8CrossRefGoogle Scholar
  86. 86.
    Blasco J, Cubero S, Gómez-Sanchis J, Mira P, Moltó E (2009) Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J Food Eng 90:27–34CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • José Blasco
    • 1
  • Sandra Munera
    • 1
  • Nuria Aleixos
    • 2
  • Sergio Cubero
    • 1
  • Enrique Molto
    • 1
    Email author
  1. 1.IVIA, Centro de AgroingenieríaMoncadaSpain
  2. 2.Departamento de Ingeniería GráficaUniversitat Politècnica de ValènciaValenciaSpain

Personalised recommendations