Skip to main content
Log in

Applications of computer vision techniques in the agriculture and food industry: a review

  • Review Paper
  • Published:
European Food Research and Technology Aims and scope Submit manuscript

Abstract

Over the last decades, parallel to technological development, there has been a great increase in the use of visual inspection systems. These systems have been widely implemented, particularly in the stage of inspection of product quality, as a means of replacing manual inspection conducted by humans. Much research has been published proposing the use of such tools in the processes of sorting and classification of food products. This paper presents a review of the main publications in the last ten years with respect to new technologies and to the wide application of systems of visual inspection in the sectors of precision farming and in the food industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Conci A, Azevedo E, Leta FR (2008) Computação Gráfica—Teoria e Prática, vol 2. Campus Elsevier, Rio de Janeiro

    Google Scholar 

  2. Koschan A, Abidi M (2008) Digital color image processing. Wiley, New York

    Book  Google Scholar 

  3. Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, 2nd edn. Gatesmark Publishing, Knoxville

    Google Scholar 

  4. Rodenacker K, Bengtsson E (2003) A feature set for cytometry on digitized microscopic images. Anal Cell Pathol 25(1):1–36

    Google Scholar 

  5. Tellaeche A, BurgosArtizzu XP, Pajaresc G, Ribeiro A, Quintanilla CF (2008) A new vision-based approach to differential spraying in precision agriculture. Comput Electron Agric 60:144–155

    Article  Google Scholar 

  6. Søgaard HT, Olsen HJ (2003) Determination of crop rows by image analysis without segmentation. Comput Electron Agric 38:141–158

    Article  Google Scholar 

  7. Bakker T, Wouters H, van Asselt K, Bontsema J, Tang L, Muller J, van Straten G (2008) A vision based row detection system for sugar beet. Comput Electron Agric 60:87–95

    Article  Google Scholar 

  8. Tellaeche A, BurgosArtizzu XP, Pajaresc G, Ribeiro A, Quintanilla CF (2011) A computer vision approach for weeds identification through Support Vector Machines. Appl Soft Comput 11:908–915

    Article  Google Scholar 

  9. Burgos-Artizzu XP, Ribeiro A, Tellaeche A, Pajares G, Fernández-Quintanilla C (2010) Analysis of natural images processing for the extraction of agricultural elements. Image Vis Comput 28:138–149

    Article  Google Scholar 

  10. Burgos-Artizzu XP, Ribeiro A, Tellaeche A, Pajares G, Fernández-Quintanilla C (2011) Real-time image processing for crop/weed discrimination in maize fields. Comput Electron Agric 75:337–346

    Article  Google Scholar 

  11. Cavani FA, Sousa RV, Porto AJV, Tronco ML (2006) Segmentação e Classificação de Imagens de Laranjeiras Utilizando Jseg e Perceptron Multicamadas. Minerva 3(2):189–197

    Google Scholar 

  12. Noordam JC, Otten GW, Timmermans AJM, Zwol BH (2000) High speed potato grading and quality inspection based on a colour vision system. Proc SPIE 3966:206–217. Machine Vision Applications in Industrial Inspection VIII, Kenneth W. Tobin

    Google Scholar 

  13. Cabrera RR, Juarez IL, Sheng-Jen H (2008) An analysis in a vision approach for potato inspection. J Appl Res Technol 6(2):106–119

    Google Scholar 

  14. Barnes M, Duckett T, Cielniak G, Stroud G, Harper G (2010) Visual detection of blemishes in potatoes using minimalist boosted classifiers. J Food Eng 98:339–346

    Article  Google Scholar 

  15. Davies ER (2009) The application of machine vision to food and agriculture: a review. Imaging Sci J 57(4):197–217

    Article  Google Scholar 

  16. Visen NS, Shashidhar NS, Paliwal J, Jayas DS (2001) Identification and segmentation of occluding groups of grain kernels in a grain sample image. J Agric Eng Res 79:159–166

    Article  Google Scholar 

  17. Paliwal J, Visen NS, Jayas DS (2001) Evaluation of neural network architectures for cereal grain classification using morphological features. J Agric Eng Res 79(4):361–370

    Article  Google Scholar 

  18. Paliwal J, Visen NS, Jayas DS, White NDG (2002) Specialist neural networks for cereal grain classification. Biosyst Eng 82(2):151–159

    Article  Google Scholar 

  19. Paliwal J, Visen NS, Jayas DS, White NDG (2003) Cereal grain and dockage identification using machine vision. Biosyst Eng 85(1):51–57

    Article  Google Scholar 

  20. Paliwal J, Visen NS, Jayas DS, White NDG (2003) Comparison of a neural network and a non-parametric classifier for grain kernel identification. Biosyst Eng 85(4):405–413

    Article  Google Scholar 

  21. Tahir AR, Neethirajan S, Jayas DS, Shahin MA, Symons SJ, White NDG (2007) Evaluation of the effect of moisture content on cereal grains by digital image analysis. Food Res Int 40:1140–1145

    Article  CAS  Google Scholar 

  22. Choudhary R, Paliwal J, Jayas DS (2008) Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosyst Eng 99:330–337

    Article  Google Scholar 

  23. Manickavasagan A, Sathya G, Jayas DS, White NDG (2008) Wheat class identification using monochrome images. J Cereal Sci 47:518–527

    Article  Google Scholar 

  24. Manickavasagan A, Sathya G, Jayas DS (2008) Comparison of illuminations to identify wheat classes using monochrome images. Comput Electron Agric 63:237–244

    Article  Google Scholar 

  25. Dana W, Ivo W (2008) Computer image analysis of seed shape and seed color for flax cultivar description. Comput Electron Agric 61:126–135

    Article  Google Scholar 

  26. Effendi Z, Ramli R, Ghani JA, Yaakob Z (2009) Development of Jatropha curcas color grading system for ripeness evaluation. Eur J Sci Res 30(4):662–669

    Google Scholar 

  27. Jahns G, Nielsen HM, Paul W (2001) Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading. Comput Electron Agric 31:17–29

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Blasco J, Aleixos N, Molto E (2007) Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. J Food Eng 81(3):535–543

    Article  Google Scholar 

  30. Riquelme MT, Barreiro P, Ruiz-Altisent M, Valero C (2008) Olive classification according to external damage using image analysis. J Food Eng 87:371–379

    Article  Google Scholar 

  31. Yimyam P, Chalidabhongse T, Sirisomboon P, Boonmung S (2005) Physical properties analysis of mango using computer vision. In: Proceedings of international conference on control, automation and systems (ICCAS’ 05), 2–5 June, Korea

  32. Blasco J, Cubero-García S, Alegre-Sosa S, Gómez-Sanchís J, López-Rubira V, Moltó E (2008) Short communication. Automatic inspection of the pomegranate (Punica granatum L.) arils quality by means of computer vision, Spanish. J Agric Eng 6(1):12–16

    Google Scholar 

  33. Blasco J, Cubero S, Gómez-Sanchis J, Mira P, Molto E (2009) Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J Food Eng 90:27–34

    Article  Google Scholar 

  34. Liming X, Yanchao Z (2010) Automated strawberry grading system based on image processing. Comput Electron Agric 71S:S32–S39

    Article  Google Scholar 

  35. Jarimopas B, Jaisin N (2008) An experimental machine vision system for sorting sweet tamarind. J Food Eng 89:291–297

    Article  Google Scholar 

  36. Louro AHF, Mendonça MM, Gonzaga A (2006) Classificação de tomates utilizando redes neurais artificiais, In: Proceedings of the II Workshop de Visão Computacional, São Carlos, SP

  37. Lino ACL, Sanches J, Fabbro IMD (2008) Image processing techniques for lemons and tomatoes classification. Bragantia Campinas 67(3):785–789

    Article  Google Scholar 

  38. Blasco J, Aleixos N, Gomez J, Molto E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83:384–393

    Article  Google Scholar 

  39. Blasco J, Aleixos N, Gómez-Sanchis J, Molto E (2009) Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosyst Eng 103:137–145

    Article  Google Scholar 

  40. Gómez J, Blasco J, Molto E, Camps-Valls G (2007) Hyperspectral detection of citrus damage with a Mahalanobis kernel classifier. Electron Lett 43:1082–1084

    Article  Google Scholar 

  41. Gómez-Sanchis J, Molto E, Camps-Valls G, Gomez-Chova L, Aleixos N, Blasco J (2008) Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. J Food Eng 85:191–200

    Article  Google Scholar 

  42. Stajnko D, Lakota M, Hocevar M (2004) Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Comput Electron Agric 42:31–42

    Article  Google Scholar 

  43. Stajnko D, Emelik Z (2005) Modelling of apple fruit growth by application of image analysis. Agric Consp Sci 70:59–64

    Google Scholar 

  44. Blasco J, Aleixos N, Molto E (2003) Machine vision system for automatic quality grading of fruit. Biosyst Eng 85(4):415–423

    Article  Google Scholar 

  45. Zou XB, Zhao JW, Li YX (2007) Apple color grading based on organization feature parameters. Pattern Recognit Lett 28:2046–2053

    Article  Google Scholar 

  46. Zou X, Zhao J (2009) On-line detecting size and color of fruit by fusing information from images of three color camera systems. In: Li D, Chunjiang Z (eds) Computer and computing technologies in agriculture II, vol 2. Springer, Boston, pp 1087–1095

    Google Scholar 

  47. Kavdir I, Guyer DE (2008) Evaluation of different pattern recognition techniques for apple sorting. Biosyst Eng 99:211–219

    Article  Google Scholar 

  48. Madieta E (2003) Apple color measurements. Some metrological approaches. Acta Hort 599:337–342

    Google Scholar 

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

    Article  Google Scholar 

  50. Bennedsen BS, Peterson DL, Tabb A (2005) Identifying defects in images of rotating apples. Comput Electron Agric 48:92–102

    Article  Google Scholar 

  51. Kleynen O, Leemans V, Destain MF (2005) Development of a multispectral vision system for the detection of defects on apples. J Food Eng 69:41–49

    Article  Google Scholar 

  52. Throop JA, Aneshansley DJ, Anger WC, Peterson DL (2005) Quality evaluation of apples based on surface defects of an automated inspection system. Postharvest Biol Technol 36:281–290

    Article  Google Scholar 

  53. 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 70:129–134

    Article  Google Scholar 

  54. Kang SP, East AR, Trujillo FJ (2008) Colour vision system evaluation of bicolour fruit: a case study with ‘B74′ mango. Postharvest Biol Technol 49:77–85

    Article  Google Scholar 

  55. Rocha A, Hauagge DC, Wainer J, Goldenstein S (2008) Automatic produce classification from images using color, texture and appearance cues. Anais do XXI Brazilian Symposium on Computer Graphics and Image Processing

  56. Rocha A, Hauagge DC, Wainer J, Goldenstein S (2010) Automatic fruit and vegetable classification from images. Comput Electron Agric 70:96–104

    Article  Google Scholar 

  57. O’Sullivan MG, Byrne DV, Martens H, Gidskehaug LH, Andersen HJ, Martens M (2003) Evaluation of pork colour: prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. Meat Sci 65:909–918

    Article  Google Scholar 

  58. Jackman P, Sun DW, Du C, Allen P, Downey G (2008) Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Sci 80:1273–1281

    Article  Google Scholar 

  59. Valous NA, Mendoza F, Sun DW, Allen P (2009) Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat Sci 81:132–141

    Article  Google Scholar 

  60. Valous NA, Mendoza F, Sun DW (2010) Emerging noncontact imaging, spectroscopic and colorimetric technologies for quality evaluation and control of hams: a review. Trends Food Sci Technol 21:26–43

    Article  CAS  Google Scholar 

  61. Louka N, Juhel F, Fazilleau V, Loonis P (2004) A novel colorimetry analysis used to compare different drying fish processes. Food Control 15:327–334

    Article  CAS  Google Scholar 

  62. Misimi E, Erikson U, Skavhaug A (2008) Quality Grading of Atlantic Salmon (Salmo salar) by Computer Vision. J Food Sci 73(5):211–217

    Article  Google Scholar 

  63. Pedreschi F, Leon J, Mery D, Moyano P (2006) Development of a computer vision system to measure the color of potato chips. Food Res Int 39:1092–1098

    Article  Google Scholar 

  64. Jusoh YMM, Chin NL, Yusof YA, Rahman RA (2009) Bread crust thickness measurement using digital imaging and Lab colour system. J Food Eng 94:366–371

    Article  Google Scholar 

  65. Jelinski T, Du CJ, Sun DW, Fornal J (2007) Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision. J Food Eng 83:3–9

    Article  Google Scholar 

  66. Martin MLG, Ji W, Luo R, Hutchings J, Heredia FJ (2007) Measuring colour appearance of red wines. Food Qual Prefer 18:862–871

    Article  Google Scholar 

  67. Du CJ, Sun DW (2005) Comparison of three methods for classification of pizza topping using different colour space transformations. J Food Eng 66:277–287

    Article  Google Scholar 

  68. Munkevik P, Hall G, Duckett T (2007) A computer vision system for appearance-based descriptive sensory evaluation of meals. J Food Eng 78:246–256

    Article  Google Scholar 

  69. BIPM, IEC, IFCC, ILAC, IUPAC, IUPAP, ISO, OIML (2012) The international vocabulary of metrology—basic and general concepts and associated terms (VIM), 3rd edn. JCGM 200:2012. http://www.bipm.org/vim

  70. Simões AS, Costa AHR (2003) Classificação de laranjas baseada em padrões visuais. 6º Simpósio Brasileiro de Automação Inteligente (SBAI). Sociedade Brasileira de Automática. Bauru, setembro 14–17, pp. 77–81

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juliana Freitas Santos Gomes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gomes, J.F.S., Leta, F.R. Applications of computer vision techniques in the agriculture and food industry: a review. Eur Food Res Technol 235, 989–1000 (2012). https://doi.org/10.1007/s00217-012-1844-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00217-012-1844-2

Keywords

Navigation