Skip to main content
SpringerLink
Log in

Monitoring Visual Properties of Food in Real Time During Food Drying

  • Published:
Food Engineering Reviews Aims and scope Submit manuscript

Abstract

Annually , one-third of the food produced globally is lost or wasted. A considerable portion of global food waste comprises dry foods that are rejected due to their unattractive appearance. One effective technique to solve this problem is by developing dryers that consistently produce dry foods that are visually appealing and have a long shelf life. The beating heart of such dryers is a computer vision (CV) system that monitors the visual attributes of the food, in real time, during the drying process. Unfortunately, there are currently no real-time CV systems for monitoring the visual attributes of food during fluidized bed drying. This setback is linked to figure-ground separation challenges encountered while segmenting real-time images of the food. Sadly, when current CV systems are used to monitor visual attributes of food during fluidized bed drying, these CV systems fail miserably because they are not designed to account for three major dryer-dependent determinants—the layout, the state and pattern of motion, and the behavior of food materials within the image captured during fluidized bed drying. To solve this lingering problem, this paper reviewed various computer vision systems based on the three determinants. This study revealed that input images for the different CV systems can be categorized as being either static-type images or chaotic-type images. The CV systems were grouped into “Static-input offline CV systems,” “Static-input online CV systems,” and “Chaotic-input online CV systems.” Building on the insight gained while reviewing the three classes of CV systems, two novel AI-driven solutions for monitoring visual attributes of food, in real time, during fluidized bed drying were proposed. The first solution was a “two-pass” deep learning system that predicts visual attributes from segmented results. While the second solution was a “single-pass” deep learning system that by-passes the segmentation step, thus saving computational cost. When such AI-driven solutions are merged with a control system and then integrated with fluidized bed dryers, this union could open the gateway to intelligent drying, where dryers consistently produce high-quality dry foods. By extension, consistency in product quality could reduce global food losses and waste significantly.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Iheonye A, Gariepy Y, Raghavan V (2019) Computer vision for real-time monitoring of shrinkage for peas dried in a fluidized bed dryer. Drying Technol pp 1–17. https://doi.org/10.1080/07373937.2019.1649277

  2. United Nations World Population Prospects (2019) Highlights. United Nations, New York, p 2019

    Google Scholar 

  3. Willenbockel D (2014) In Scenarios for global agriculture and food security towards 2050: a review of recent studies. Routledge, New challenges to food security, pp 55–76

    Google Scholar 

  4. Serraj R, Pingali P (2019) Agriculture & food systems to 2050; World Scientific Publishing Co Pte Ltd: Singapore

  5. Stępień S, Dobrowolski D (2017) Loss and waste in the food supply chain: an introduction to the problem. Prog Econ Sci. https://doi.org/10.14595/PES/04/022

  6. FAO (2018) The future of food and agriculture – Alternative pathways to 2050. Food and Agriculture Organization of the United Nations (FAO). p 224

  7. FAO (2019) State of food and agriculture: moving forward on food loss and waste reduction.

  8. Environment and Climate Change Canada (2019) Taking stock: reducing food loss and waste in Canada; Waste reduction and Management Division, Environment and Climate Change Canada

  9. FAO Food loss and food waste. http://www.fao.org/food-loss-and-food-waste/en/ (Accessed August 10, 2019)

  10. FAO Global initiative on food loss and waste reduction. http://www.fao.org/3/a-i4068e.pdf (Accessed August 10, 2019)

  11. FAO Key facts on food loss and waste you should know. http://www.fao.org/save-food/resources/keyfindings/en/

  12. Julian P, Mark B, Sarah M (2010) Food waste within food supply chains: quantification and potential for change to 2050. Philos Trans Royal Soc B: Biol Sci https://doi.org/10.1098/rstb.2010.0126

    Article  Google Scholar 

  13. Mayor L, Moreira R, Sereno AM (2011) Shrinkage, density, porosity and shape changes during dehydration of pumpkin (Cucurbita pepo L.) fruits. J Food Eng 103:29–37. https://doi.org/10.1016/j.jfoodeng.2010.08.031

  14. Demirhan E, Ozbek B (2009) Color change kinetics of microwave-dried basil. Drying Technol 27:156–166. https://doi.org/10.1080/07373930802566101

    Article  Google Scholar 

  15. Nicolas JJ, Richard-Forget FC, Goupy PM, Amiot MJ, Aubert SY (1994) Enzymatic browning reactions in apple and apple products. Crit Rev Food Sci Nutr 34:109–157. https://doi.org/10.1080/10408399409527653

    Article  CAS  PubMed  Google Scholar 

  16. Oliveira SM, Brandão TRS, Silva CLM (2015) Influence of drying processes and pretreatments on nutritional and bioactive characteristics of dried vegetables: a review. Food Eng Rev 8:134–163. https://doi.org/10.1007/s12393-015-9124-0

    Article  CAS  Google Scholar 

  17. Martynenko A (2017) Computer vision for real-time control in drying. Food Eng Rev 9:91–111. https://doi.org/10.1007/s12393-017-9159-5

    Article  Google Scholar 

  18. Gunasekaran S (1996) Computer vision technology for food quality assurance. Trends Food Sci Technol 7:245–256. https://doi.org/10.1016/0924-2244(96)10028-5

    Article  CAS  Google Scholar 

  19. Du CJ, Sun DW (2004) Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15:230–249. https://doi.org/10.1016/j.tifs.2003.10.006

    Article  CAS  Google Scholar 

  20. Chen Y, Sun D, Cheng J, Gao W (2016) Recent advances for rapid identification of chemical information of muscle foods by hyperspectral imaging analysis. Food Eng Revs 8:336–350. https://doi.org/10.1007/s12393-016-9139-1

    Article  CAS  Google Scholar 

  21. Du CJ, Sun DW (2006) Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng 72:39–55. https://doi.org/10.1016/j.jfoodeng.2004.11.017

    Article  Google Scholar 

  22. Brosnan T, Sun D (2004) Improving quality inspection of food products by computer vision – a review. J Food Eng 61:3–16

    Article  Google Scholar 

  23. Patel K, Kar A (2012) Heat pump assisted drying of agricultural produce—an overview. J Food Sci Technol 49:142–160. https://doi.org/10.1007/s13197-011-0334-z

    Article  PubMed  Google Scholar 

  24. Cubero S, Aleixos N, Molto E, Gomez-Sanchis J, Blasco J (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4:487–504. https://doi.org/10.1007/s11947-010-0411-8

    Article  Google Scholar 

  25. Zareiforoush H, Minaei S, Alizadeh MR, Banakar A (2015) Potential applications of computer vision in quality inspection of rice: a review. Food Eng Rev 7:321–345. https://doi.org/10.1007/s12393-014-9101-z

    Article  Google Scholar 

  26. Guine R, Barroca MJ (2012) Effect of drying treatments on texture and color of vegetables (pumpkin and green pepper). Food Bioprod Process 90:58–63. https://doi.org/10.1016/j.fbp.2011.01.003

    Article  Google Scholar 

  27. Krokida MK, Tsami E, Maroulis ZB (1998) Kinetics on color changes during drying of some fruits and vegetables. Drying Technol 16:667–685. https://doi.org/10.1080/07373939808917429

    Article  CAS  Google Scholar 

  28. Zielinska M, Markowski M (2012) Color characteristics of carrots: effect of drying and rehydration. Int J Food Prop 15:450–466. https://doi.org/10.1080/10942912.2010.489209

    Article  CAS  Google Scholar 

  29. Fernández L, Castillero C, Aguilera JM (2005) An application of image analysis to dehydration of apple discs. J Food Eng 67:185–193. https://doi.org/10.1016/j.jfoodeng.2004.05.070

    Article  Google Scholar 

  30. Aral S, Beşe AV (2016) Convective drying of hawthorn fruit (Crataegus spp.): effect of experimental parameters on drying kinetics, color, shrinkage, and rehydration capacity. Food Chem 210:577–584

  31. Huang M, Wang Q, Zhang M, Zhu Q (2014) Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. J Food Eng 128:24–30. https://doi.org/10.1016/j.jfoodeng.2013.12.008

    Article  Google Scholar 

  32. Priddy KL, Keller PE (2005) Artificial neural networks: an introduction. SPIE

  33. Nahimana H, Zhang M (2011) Shrinkage and color change during microwave vacuum drying of carrot. Drying Technol 29:836–847. https://doi.org/10.1080/07373937.2011.573753

    Article  CAS  Google Scholar 

  34. Fathi M, Fathi M, Mohebbi M, Mohebbi M, Razavi S, Razavi S (2011) Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food Bioprocess Technol 4:1357–1366. https://doi.org/10.1007/s11947-009-0222-y

    Article  Google Scholar 

  35. Zenoozian MS, Devahastin S, Razavi MA, Shahidi F, Poreza HR (2008) Use of artificial neural network and image analysis to predict physical properties of osmotically dehydrated pumpkin. Drying Technol 26:132–144. https://doi.org/10.1080/07373930701781793

    Article  CAS  Google Scholar 

  36. Yadollahinia A, Jahangiri M (2009) Shrinkage of potato slice during drying. J Food Eng 94:52–58. https://doi.org/10.1016/j.jfoodeng.2009.02.028

    Article  Google Scholar 

  37. Hosseinpour S, Rafiee S, Mohtasebi SS, Aghbashlo M (2013) Application of computer vision technique for on-line monitoring of shrimp color changes during drying. J Food Eng 115:99–114. https://doi.org/10.1016/j.jfoodeng.2012.10.003

    Article  Google Scholar 

  38. Yadollahinia A, Latifi A, Mahdavi R (2009) New method for determination of potato slice shrinkage during drying. Comput Electron Agric 65:268–274. https://doi.org/10.1016/j.compag.2008.11.003

    Article  Google Scholar 

  39. Wang D, Martynenko A, Corscadden K, He Q (2017) Computer vision for bulk volume estimation of apple slices during drying. Drying Technol 35:616–624. https://doi.org/10.1080/07373937.2016.1196700

    Article  Google Scholar 

  40. Hosseinpour S, Rafiee S, Mohtasebi SS (2011) Application of image processing to analyze shrinkage and shape changes of shrimp batch during drying. Drying Technol 29:1416–1438. https://doi.org/10.1080/07373937.2011.587620

    Article  Google Scholar 

  41. Romano G, Argyropoulos D, Nagle M, Khan MT, Muller J (2012) Combination of digital images and laser light to predict moisture content and color of bell pepper simultaneously during drying. J Food Eng 109:438–448. https://doi.org/10.1016/j.jfoodeng.2011.10.037

    Article  Google Scholar 

  42. Martynenko AI, Davidson VJ, Brown RB (20052) Intelligent computer vision system (SAIF) for automated inspection of ginseng root quality. Presented at the CSAE/SCGR 2005 Meeting, Winnipeg, Manitoba

  43. Hosseinpour S, Rafiee S, Aghbashlo M, Mohtasebi SS (2014) A novel image processing approach for in-line monitoring of visual texture during shrimp drying. J Food Eng 143:154–166. https://doi.org/10.1016/j.jfoodeng.2014.07.003

    Article  Google Scholar 

  44. Nadian MH, Nadian MH, Abbaspour-Fard MH, Sadrnia H, Golzarian MR, Tabasizadeh M, Martynenko A, Martynenko A (2017) Improvement of kiwifruit drying using computer vision system (CVS) and ALM clustering method. Drying Technol 35:709–723. https://doi.org/10.1080/07373937.2016.1208665

    Article  CAS  Google Scholar 

  45. Nadian MH, Rafiee S, Aghbashlo M, Hosseinpour S, Mohtasebi SS (2015) Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food Bioprod Process 94:263–274. https://doi.org/10.1016/j.fbp.2014.03.005

    Article  Google Scholar 

  46. Saldaña E, Siche R, Huamán R, Luján M, Castro W, Quevedo R (2013) Computer vision system in real-time for color determination on flat surface food. Scientia Agropecuaria 4:55–63. https://doi.org/10.17268/sci.agropecu.2013.01.06

    Article  Google Scholar 

  47. Martynenko AI (2006) Computer-vision system for control of drying processes. Drying Technol 24:879–888. https://doi.org/10.1080/07373930600734067

    Article  Google Scholar 

  48. Gonzalez RC, Woods RE (2018) Digital image processing; Pearson: New York, NY

  49. Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB. Gatesmark Publ, Natick, Mass

    Google Scholar 

  50. Buishand JG, Gabelman WH (1979) Investigations on the inheritance of color and carotenoid content in phloem and xylem of carrot roots (Daucus carota L.). Euphytica 28:611–632. https://doi.org/10.1007/BF00038928

  51. Zenoozian MS, Devahastin S (2009) Application of wavelet transform coupled with artificial neural network for predicting physicochemical properties of osmotically dehydrated pumpkin. J Food Eng 90:219–227. https://doi.org/10.1016/j.jfoodeng.2008.06.033

    Article  Google Scholar 

  52. Gao X, Tan J (1996) Analysis of expanded-food texture by image processing. 1. Geometric properties. J Food Process Eng 19:425–444. https://doi.org/10.1111/j.1745-4530.1996.tb00403.x

  53. Možina M, Tomaževič D, Leben S, Pernuš F, Likar B (2010) Digital imaging as a process analytical technology tool for fluid-bed pellet coating process. Eur J Pharm Sci 41:156–162. https://doi.org/10.1016/j.ejps.2010.06.001

    Article  CAS  PubMed  Google Scholar 

  54. Kucheryavski S, Esbensen KH, Bogomolov A (2010) Monitoring of pellet coating process with image analysis—a feasibility study. J Chemom 24:472–480. https://doi.org/10.1002/cem.1292

    Article  CAS  Google Scholar 

  55. Chen Y, Martynenko A (2013) Computer vision for real-time measurements of shrinkage and color changes in blueberry convective drying. Drying Technol 31:1114–1123. https://doi.org/10.1080/07373937.2013.775587

    Article  CAS  Google Scholar 

  56. Tousey MD (2003) The granulation process 101: basic technologies for tablet making. Pharm Technol 2002(26):S8

    Google Scholar 

  57. Chopra R, Alderborn G, Podczeck F, Newton JM (2002) The influence of pellet shape and surface properties on the drug release from uncoated and coated pellets. Int J Pharm 239:171–178. https://doi.org/10.1016/S0378-5173(02)00104-7

    Article  CAS  PubMed  Google Scholar 

  58. Chan LW, Chan WY, Heng PWS (2001) An improved method for the measurement of colour uniformity in pellet coating. Int J Pharm 213:63–74. https://doi.org/10.1016/S0378-5173(00)00619-0

    Article  CAS  PubMed  Google Scholar 

  59. Oman N, Možina M, Tomaževič D, Pernuš F, Likar B (2011) A study of a visual inspection technique for in-process monitoring of coating of pharmaceutical pellets. Presented at the 2011 IEEE International Conference on Industrial Technology, Auburn, AL, USA

  60. Watano S, Miyanami K (1995) Image processing for on-line monitoring of granule size distribution and shape in fluidized bed granulation. Powder Technol 83:55–60. https://doi.org/10.1016/0032-5910(94)02944-J

    Article  CAS  Google Scholar 

  61. Mozina M, Burmen M, Tomazevic D, Pernus F, Likar B (2009) Automatic visual inspection of pharmaceutical pellets in coating process. Presented at the 2009.

  62. Oman Kadunc N, Šibanc R, Dreu R, Likar B, Tomaževič D (2014) In-line monitoring of pellet coating thickness growth by means of visual imaging. Int J Pharm 470:8–14. https://doi.org/10.1016/j.ijpharm.2014.04.066

    Article  CAS  PubMed  Google Scholar 

  63. Iheonye A, Gariepy Y, Raghavan V (2019) Real-time monitoring of fluidized bed drying using computer vision. Presented at the, (2019) ASABE Annual International Meeting. Massachusetts, USA, Boston

    Google Scholar 

  64. Ronneberger O, Fischer P, Brox T (2015) In U-Net: convolutional networks for biomedical image segmentation; medical image computing and computer-assisted intervention – MICCAI. Springer International Publishing: Cham 2015:234–241

    Google Scholar 

  65. Chen H, Qi X, Yu L, Heng PA (2016) DCAN: deep contour-aware networks for accurate gland segmentation. Presented at the 2016

  66. Chen H, Qi X, Yu L, Dou Q, Qin J, Heng P (2017) DCAN: deep contour-aware networks for object instance segmentation from histology images. Med Image Anal 36:135–146. https://doi.org/10.1016/j.media.2016.11.004

    Article  PubMed  Google Scholar 

  67. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Machine Intell 40:834–848. https://doi.org/10.1109/TPAMI.2017.2699184

  68. He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. Presented at the 2016

  69. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning

  70. Cornell University Library (2015) arXiv.org. Very deep convolutional networks for large-scale image recognition. arXiv.org

  71. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A, Chang JH (2015) Going deeper with convolutions. Presented at the Jun 2015

  72. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. Presented at the Jul 2017

  73. Russell BC, Torralba A, Murphy KP, Freeman WT (2007) LabelMe: a database and web-based tool for image annotation. Int J Comput Vision 77:157–173. https://doi.org/10.1007/s11263-007-0090-8

    Article  Google Scholar 

  74. Géron A (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow; O’Reilly Media: Sebastopol

Download references

Acknowledgements

The authors acknowledge the doctoral scholarship from the Tertiary Education Trust Fund (TETFund) of Nigeria and the financial support of the Discovery Grant Program of the Natural Sciences and Engineering Research Council of Canada (NSERC).

Funding

Tertiary Education Trust Fund,Natural Sciences and Engineering Research Council of Canada

Author information

Authors and Affiliations

  1. Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, H9X 3V9, Canada

    Anthony C. Iheonye, Vijaya Raghavan, Valérie Orsat & Yvan Gariepy

  2. Department of Electrical and Computer Engineering, McConnell Engineering Building, McGill University, 3480 University Street, Montreal, QC, H3A 2E9, Canada

    Frank P. Ferrie

Authors
  1. Anthony C. Iheonye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijaya Raghavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank P. Ferrie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Valérie Orsat
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yvan Gariepy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to writing and reviewing the different sections of the manuscript.

Corresponding author

Correspondence to Vijaya Raghavan.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iheonye, A.C., Raghavan, V., Ferrie, F.P. et al. Monitoring Visual Properties of Food in Real Time During Food Drying. Food Eng Rev 15, 242–260 (2023). https://doi.org/10.1007/s12393-023-09334-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12393-023-09334-6

Keywords

Search

Navigation