Skip to main content
SpringerLink
Log in

Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing

  • Published:
Food Engineering Reviews Aims and scope Submit manuscript

Abstract

Today in the agricultural industry, many defects affect production efficiency; this paper aims to show how the combination of machine vision (MV) and image processing (IP) helps to detect the defective areas of products. Defects generally appear due to insect damage, scarring, product decay, and so on. In this review, the importance of quality inspection in the agricultural industry and its effect on worldwide markets are highlighted and the ways which help to categorize the products by their defections. In the first step, as long as agricultural products are harvested, in a suitable condition with good illumination, they are photographed by special cameras and evaluated by the IP science. In the next step, they can be classified based on the detected defection. Many classification algorithms allow us to categorize products based on the quality and type of their defects. Using a combination of MV and IP, followed by the use of special classification algorithms, helps to have more efficiency in the detection of defects in harvested products.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Dubey SR, Jalal A (2016) Apple disease classification using color, texture and shape features from images. SIViP 10:819–826. https://doi.org/10.1007/s11760-015-0821-1

    Article  Google Scholar 

  2. Mebatsion HK, Paliwal J, Jayas DS (2013) Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Comput Electron Agric 90:99–105. https://doi.org/10.1016/j.compag.2012.09.007

    Article  Google Scholar 

  3. Mittal S, Dutta MK, Issac A (2019) Non-destructive image processing based system for assessment of rice quality and defects for classification according to inferred commercial value. Measurement 148:106969. https://doi.org/10.1016/j.measurement.2019.106969

    Article  Google Scholar 

  4. Ramirez-Paredes J-P, Hernandez-Belmonte U-H (2020) Visual quality assessment of malting barley using color, shape and texture descriptors. Comput Electron Agric 168:105110. https://doi.org/10.1016/j.compag.2019.105110

    Article  Google Scholar 

  5. Raghavendra A, Guru DS, Rao MK (2021) Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy. Artif Intell Agric 5:43–51. https://doi.org/10.1016/j.aiia.2021.01.005

    Article  Google Scholar 

  6. Yogesh Dubey AK, Arora RR, Mathur A (2021) Fruit Defect Prediction Model (FDPM) based on three-level validation. J Nondestruct Eval 40(2):1–12. https://doi.org/10.1007/s10921-021-00778-6

    Article  Google Scholar 

  7. Van De Looverbosch T, Raeymaekers E, Verboven P et al (2021) Non-destructive internal disorder detection of conference pears by semantic segmentation of X-ray CT scans using deep learning. Expert Syst Appl 176:114925. https://doi.org/10.1016/j.eswa.2021.114925

  8. Kim T, Lee J, Sun GM et al (2022) Comparison of X-ray computed tomography and magnetic resonance imaging to detect pest-infested fruits: A pilot study. Nucl Eng Technol 54:514–522. https://doi.org/10.1016/j.net.2021.07.015

    Article  Google Scholar 

  9. Fu Y, Wang Y, Lin W et al (2022) A novel non-destructive detection of deteriorative dried longan fruits using machine learning algorithms based on low field nuclear magnetic resonance. J Food Meas Charact 16:652–661. https://doi.org/10.1007/s11694-021-01190-4

    Article  Google Scholar 

  10. Ariana DP, Lu R, Guyer DE (2006) Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Comput Electron Agric 53(1):60–70. https://doi.org/10.1016/j.compag.2006.04.001

    Article  Google Scholar 

  11. Zhang H, Zhang S, Dong W, Luo W, Huang Y, Zhan B, Liu X (2020) Detection of common defects on mandarins by using visible and near infrared hyperspectral imaging. Infrared Phys Technol 108:103341. https://doi.org/10.1016/j.infrared.2020.103341

    Article  CAS  Google Scholar 

  12. Munera S, Gómez-Sanchís J, Aleixos N, et al (2021) Discrimination of common defects in loquat fruit cv. “Algerie” using hyperspectral imaging and machine learning techniques. Postharvest Biol Technol 171. https://doi.org/10.1016/j.postharvbio.2020.111356

  13. Gui J, Fei J, Wu Z, Fu X, Diakite A (2020) Grading method of soybean mosaic disease based on hyperspectral imaging technology. Inf Process Agric. https://doi.org/10.1016/j.inpa.2020.10.006

    Article  Google Scholar 

  14. Jia B, Wang W, Ni X, Lawrence KC, Zhuang H, Yoon S-C, Gao Z (2020) Essential processing methods of hyperspectral images of agricultural and food products. Chemom Intell Lab Syst 198:103–936. https://doi.org/10.1016/j.chemolab.2020.103936

    Article  CAS  Google Scholar 

  15. Liu D, Ning X, Li Z, Yang D, Li H, Gao L (2015) Discriminating and elimination of damaged soybean seeds based on image characteristics. J Stored Prod Res 60:67–74. https://doi.org/10.1016/j.jspr.2014.10.001

    Article  Google Scholar 

  16. Arjenaki OO, Moghaddam PA, Motlagh AM (2013) Online tomato sorting based on shape, maturity, size, and surface defects using machine vision. Turkish J Agric For 37:62–68. https://doi.org/10.3906/tar-1201-10

    Article  Google Scholar 

  17. Zheng C, Sun D-W, Zheng L (2006) Recent applications of image texture for evaluation of food qualities–a review. Trends Food Sci Technol 17(3):113–128. https://doi.org/10.1016/j.tifs.2005.11.006

    Article  CAS  Google Scholar 

  18. Anjali N, Kannan R, Andres F, Ghinea G (2021) Trend review related to defects detection from fruits and vegetables. Preprints, 2021110035. https://doi.org/10.20944/preprints202111.0035.V1

  19. Lu Y, Lu R (2017) Non-destructive defect detection of apples by spectroscopic and imaging technologies: a review. Transactions of the ASABE (Am Soc Agric Biol Eng) 60. https://doi.org/10.13031/trans.12431

  20. Nturambirwe JFI, Opara UL (2020) Machine learning applications to non-destructive defect detection in horticultural products. Biosys Eng 189:60–83. https://doi.org/10.1016/j.biosystemseng.2019.11.011

    Article  Google Scholar 

  21. Zheng Y, Ma Y, Liu W, Qiu F (2020) Chapter 4 – plant nutrition and physiological disorders in fruit crops. In: Srivastava AK, Hu C (eds) Fruit Crops. Elsevier, p 47–58. https://doi.org/10.1016/b978-0-12-818732-6.00004-6

  22. Gustavsson J, Cederberg C, Sonesson U, van Otterdijk R, Meybeck A (2011) Global food losses and food waste: Extent, causes and prevention. FAO, Rome, Italy

    Google Scholar 

  23. Tripathi MK, Maktedar DD (2020) A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey. Inf Process Agric 7:183–203. https://doi.org/10.1016/j.inpa.2019.07.003

    Article  Google Scholar 

  24. Shahin M, Tollner E, Gitaitis R, Sumner D, Maw B (2002) Classification of sweet onions based on internal defects using image processing and neural network techniques. Trans ASAE 45. https://doi.org/10.13031/2013.11046

  25. Wang Z, Hu M-H, Zhai G (2018) Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors (Basel, Switzerland) 18. https://doi.org/10.3390/s18041126

  26. 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. Biosys Eng 122:91–98. https://doi.org/10.1016/j.biosystemseng.2014.03.009

    Article  Google Scholar 

  27. Wang Y, Chen Y (2020) Fruit morphological measurement based on three-dimensional reconstruction. Agronomy 10(4):455 (https://www.mdpi.com/2073-4395/10/4/455)

    Article  Google Scholar 

  28. Ayaz H, Rodríguez-Esparza E, Ahmad M, Oliva D, Pérez-Cisneros M, Sarkar R (2021) Classification of apple disease based on non-linear deep features. Appl Sci 11(14):6422. https://www.mdpi.com/2076-3417/11/14/6422

    Article  CAS  Google Scholar 

  29. Alex S, Premkumar S (2021) Detection of fungal disease in cabbage images using adaptive thresholding technique compared with threshold technique. Revista geintec-gestao inovacao e tecnologias

  30. Nicolaï BM, Lötze E, Peirs A, Scheerlinck N, Theron KI (2006) Non-destructive measurement of bitter pit in apple fruit using nir hyperspectral imaging. Postharvest Biol Technol 40(1):1–6. https://doi.org/10.1016/j.postharvbio.2005.12.006

    Article  Google Scholar 

  31. van Dael M, Lebotsa S, Herremans E, Verboven P, Sijbers J, Opara UL, Cronje PJ, Nicolaï BM (2016) A segmentation and classification algorithm for online detection of internal disorders in citrus using x-ray radiographs. Postharvest Biol Technol 112:205–214. https://doi.org/10.1016/j.postharvbio.2015.09.020

    Article  Google Scholar 

  32. Cen H, Lu R, Zhu Q, Mendoza F (2016) Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification. Postharvest Biol Technol 111:352–361. https://doi.org/10.1016/j.postharvbio.2015.09.027

    Article  Google Scholar 

  33. Zhao J, Qu J (2019, 23–25 Aug) A detection method for tomato fruit common physiological diseases based on YOLOv2. In 2019 10th international conference on Information Technology in Medicine and Education (ITME)

  34. Anyasi TA, Jideani AIO, Mchau GA (2015) Morphological, physicochemical, and antioxidant profile of noncommercial banana cultivars. Food Sci Nutr 3(3):221–232. https://doi.org/10.1002/fsn3.208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Moallem P, Serajoddin A, Pourghassem H (2017) Computer vision-based apple grading for golden delicious apples based on surface features. Inf Process Agric 4(1):33–40. https://doi.org/10.1016/j.inpa.2016.10.003

    Article  Google Scholar 

  36. Jahanbakhshi A, Kheiralipour K (2020) Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit. Food Sci Nutr 8(7):3346–3352. https://doi.org/10.1002/fsn3.1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Moggia C, Pereira M, Yuri JA, Torres CA, Hernández O, Icaza MG, Lobos GA (2015) Preharvest factors that affect the development of internal browning in apples cv. Cripp’s pink: six-years compiled data. Postharvest Biol Technol 101:49–57. https://doi.org/10.1016/j.postharvbio.2014.11.005

    Article  Google Scholar 

  38. Raghavendra A, Rao M (2016) A survey on internal defect detection in fruits by non-intrusive methods. Int J Latest Trends Eng Technol 6:343–348

    Google Scholar 

  39. Van Dael M, Verboven P, Zanella A, Sijbers J, Nicolai B (2019) Combination of shape and x-ray inspection for apple internal quality control: in silico analysis of the methodology based on x-ray computed tomography. Postharvest Biol Technol 148:218–227. https://doi.org/10.1016/j.postharvbio.2018.05.020

    Article  Google Scholar 

  40. Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60. https://doi.org/10.1016/j.biosystemseng.2016.01.017

    Article  Google Scholar 

  41. Ray M, Ray A, Dash S et al (2017) Fungal disease detection in plants: Traditional assays, novel diagnostic techniques and biosensors. Biosens Bioelectron 87:708–723. https://doi.org/10.1016/j.bios.2016.09.032

    Article  CAS  PubMed  Google Scholar 

  42. Sun Y, Wang Y, Xiao H, Gu X, Pan L, Tu K (2017) Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content. Food Chem 235:194–202. https://doi.org/10.1016/j.foodchem.2017.05.064

    Article  CAS  PubMed  Google Scholar 

  43. Hussein Z, Fawole O, Opara U (2018) Preharvest factors influencing bruise damage of fresh fruits – a review. Sci Hortic 229:45–58. https://doi.org/10.1016/j.scienta.2017.10.028

    Article  Google Scholar 

  44. Li Z, Thomas C (2014) Quantitative evaluation of mechanical damage to fresh fruits. Trends Food Sci Technol 35(2):138–150. https://doi.org/10.1016/j.tifs.2013.12.001

    Article  CAS  Google Scholar 

  45. Rong D, Rao X, Ying Y (2017) Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Comput Electron Agric 137:59–68. https://doi.org/10.1016/j.compag.2017.02.027

    Article  Google Scholar 

  46. Kim G, Kim G-H, Park J, Kim D-Y, Cho B-K (2014) Application of infrared lock-in thermography for the quantitative evaluation of bruises on pears. Infrared Phys Technol 63:133–139. https://doi.org/10.1016/j.infrared.2013.12.015

    Article  Google Scholar 

  47. Cui S, Ling P, Zhu H, Keener HM (2018) Plant pest detection using an artificial nose system: a review. Sensors 18(2):378 (https://www.mdpi.com/1424-8220/18/2/378)

    Article  Google Scholar 

  48. Herremans E, Verboven P, Bongaers E, Estrade P, Verlinden BE, Wevers M, Hertog MLATM, Nicolai BM (2013) Characterisation of braeburn browning disorder by means of x-ray micro-ct. Postharvest Biol Technol 75:114–124. https://doi.org/10.1016/j.postharvbio.2012.08.008

    Article  Google Scholar 

  49. Magwaza LS, Opara UL, Terry LA, Landahl S, Cronje PJ, Nieuwoudt H, Mouazen AM, Saeys W, Nicolaï BM (2012) Prediction of ‘nules clementine’ mandarin susceptibility to rind breakdown disorder using vis/nir spectroscopy. Postharvest Biol Technol 74:1–10. https://doi.org/10.1016/j.postharvbio.2012.06.007

    Article  CAS  Google Scholar 

  50. Ito H, Fukino-Ito N, Horie H, Morimoto S (2004) Non-destructive detection of physiological disorders in melons using near infrared (NIR) spectroscopy. Acta Hortic 229–234

  51. Baranowski P, Mazurek W (2009) Detection of physiological disorders and mechanical defects in applesusing thermography. Int Agrophys 23:9–17

    Google Scholar 

  52. Jha S (2010). Nondestructive evaluation of food quality. https://doi.org/10.1007/978-3-642-15796-7

    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(1):129–134. https://doi.org/10.1016/j.compag.2009.09.014

    Article  Google Scholar 

  54. Unay D, Gosselin B, Kleynen O, Leemans V, Destain M-F, Debeir O (2011) Automatic grading of bi-colored apples by multispectral machine vision. Comput Electron Agric 75(1):204–212. https://doi.org/10.1016/j.compag.2010.11.006

    Article  Google Scholar 

  55. Mehl PM, Chen Y-R, Kim MS, Chan DE (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J Food Eng 61(1):67–81. https://doi.org/10.1016/s0260-8774(03)00188-2

    Article  Google Scholar 

  56. Gómez-Sanchis J, Moltó E, Camps-Valls G, Gómez-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 74(2):191–200. https://doi.org/10.1016/j.jfoodeng.2007.06.036

    Article  Google Scholar 

  57. Ariana D, Guyer DE, Shrestha B (2006) Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Comput Electron Agric 50(2):148–161. https://doi.org/10.1016/j.compag.2005.10.002

    Article  Google Scholar 

  58. Habib MT, Majumder A, Jakaria AZM, Akter M, Uddin MS, Ahmed F (2020) Machine vision based papaya disease recognition. J King Saud Univ Comput Inf Sci 32(3):300–309. https://doi.org/10.1016/j.jksuci.2018.06.006

    Article  Google Scholar 

  59. Li J, Huang W, Zhao C, Zhang B (2013a) A comparative study for the quantitative determination of soluble solids content, ph and firmness of pears by vis/nir spectroscopy. J Food Eng 116(2):324–332. https://doi.org/10.1016/j.jfoodeng.2012.11.007

    Article  CAS  Google Scholar 

  60. Chen S, Xiong J, Guo W, Bu R, Zheng Z, Chen Y, Yang Z, Lin R (2019) Colored rice quality inspection system using machine vision. J Cereal Sci 88:87–95. https://doi.org/10.1016/j.jcs.2019.05.010

    Article  Google Scholar 

  61. Ireri D, Belal E, Okinda C, Makange N, Ji C (2019) A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artif Intelligence Agric 2:28–37. https://doi.org/10.1016/j.aiia.2019.06.001

    Article  Google Scholar 

  62. Du Z, Zeng X, Li X et al (2020) Recent advances in imaging techniques for bruise detection in fruits and vegetables. Trends Food Sci Technol 99:133–141. https://doi.org/10.1016/j.tifs.2020.02.024

    Article  CAS  Google Scholar 

  63. Zhang B, Huang W, Li J, Zhao C, Fan S, Wu J, Liu C (2014) Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: a review. Food Res Int 62:326–343 https://doi.org/10.1016/j.foodres.2014.03.012

    Article  Google Scholar 

  64. Ran L, Zhang Y, Wei W, Zhang Q (2017) A hyperspectral image classification framework with spatial pixel pair features. Sensors (Basel, Switzerland) 17(10):2421. https://doi.org/10.3390/s17102421

    Article  Google Scholar 

  65. Li X, Li R, Wang M et al (2018) Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables. In: Hyperspectral imaging in agriculture, food and environment

  66. Giannoni L, Lange F, Tachtsidis I (2018) Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments. J Opt 2010:20

    Google Scholar 

  67. Sonka M, Hlavac V, Boyle R (1993) Image pre-processing. In: Sonka M, Hlavac V, Boyle R (eds) Image Processing, Analysis and Machine Vision. Springer US, p 56–111. https://doi.org/10.1007/978-1-4899-3216-7_4

  68. Bhargava A, Bansal A (2021) Classification and grading of multiple varieties of apple fruit. Food Anal Methods. https://doi.org/10.1007/s12161-021-01970-0

    Article  Google Scholar 

  69. Kaur D, Kaur Y (2014) Various image segmentation techniques: A review. Int J Comput Sci Mob Comput 3:809–814

    Google Scholar 

  70. Lee SU, Yoon Chung S, Park RH (1990) A comparative performance study of several global thresholding techniques for segmentation. Comput Vision Graphics Image Process 52(2):171–190. https://doi.org/10.1016/0734-189x(90)90053-x

    Article  Google Scholar 

  71. Naik S, Patel B (2017) Machine vision based fruit classification and grading - A review. Int J Comput Appl 170:22–34. https://doi.org/10.5120/ijca2017914937

    Article  Google Scholar 

  72. Leiva-Valenzuela GA, Aguilera JM (2013) Automatic detection of orientation and diseases in blueberries using image analysis to improve their postharvest storage quality. Food Control 33:166–173. https://doi.org/10.1016/j.foodcont.2013.02.025

    Article  Google Scholar 

  73. Van De Sande K, Gevers T, Snoek C (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32:1582–1596. https://doi.org/10.1109/TPAMI.2009.154

    Article  PubMed  Google Scholar 

  74. Amanatiadis A, Kaburlasos VG, Gasteratos A, Papadakis SE (2011) Evaluation of shape descriptors for shapebased image retrieval. IET Image Process 5:493–499. https://doi.org/10.1049/iet-ipr.2009.0246

    Article  Google Scholar 

  75. Chen Y, Jiang H, Li C et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54:6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

    Article  Google Scholar 

  76. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  CAS  PubMed  Google Scholar 

  77. Chen R, Wang M, Lai Y (2020) Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network. PLoS One 15:e0235783. https://doi.org/10.1371/journal.pone.0235783

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Afonso T, Moresco R, Uarrota V, Bachiega Navarro B, Nunes E, Maraschin M, Rocha M (2017) Uv-vis and cielab based chemometric characterization of manihot esculenta carotenoid contents. J Integr Bioinform 14. https://doi.org/10.1515/jib-2017-0056

  79. Leemans V, Magein H, Destain MF (2002) On-line fruit grading according to their external quality using machine vision. Biosys Eng 83(4):397–404. https://doi.org/10.1006/bioe.2002.0131

    Article  Google Scholar 

  80. Cárdenas-Pérez S, Chanona-Pérez J, Méndez-Méndez JV, Calderón-Domínguez G, López-Santiago R, Perea-Flores MJ, Arzate-Vázquez I (2017) Evaluation of the ripening stages of apple (golden delicious) by means of computer vision system. Biosys Eng 159:46–58. https://doi.org/10.1016/j.biosystemseng.2017.04.009

    Article  Google Scholar 

  81. Chauhan AP, Singh A (2012) Intelligent estimator for assessing apple fruit quality. Int J Comput Appl 60:35–41. https://doi.org/10.5120/9691-4130

    Article  Google Scholar 

  82. Wang Y, Zhang M, Mujumdar AS (2012) Influence of green banana flour substitution for cassava starch on the nutrition, color, texture and sensory quality in two types of snacks. LWT Food Sci Technol 47(1):175–182. https://doi.org/10.1016/j.lwt.2011.12.011

    Article  CAS  Google Scholar 

  83. Abdullah MZ, Mohamad-Saleh J, Fathinul-Syahir AS, Mohd-Azemi BMN (2006) Discrimination and classification of fresh-cut starfruits (averrhoa carambola l) Using automated machine vision system. J Food Eng 76(4):506–523. https://doi.org/10.1016/j.jfoodeng.2005.05.053

    Article  Google Scholar 

  84. Dorj U-O, Lee M, Yun S-S (2017) An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput Electron Agric 140:103–112. https://doi.org/10.1016/j.compag.2017.05.019

    Article  Google Scholar 

  85. Vízhányó T, Felföldi J (2000) Enhancing colour differences in images of diseased mushrooms. Comput Electron Agric 26(2):187–198. https://doi.org/10.1016/s0168-1699(00)00071-5

    Article  Google Scholar 

  86. Abdullah MZ, Guan LC, Mohamed AMD, Noor MAM (2002) Color vision system for ripeness inspection of oil palm elaeis guineensis. J Food Process Preserv 26(3):213–235. https://doi.org/10.1111/j.1745-4549.2002.tb00481.x

    Article  Google Scholar 

  87. Santos Pereira LF, Barbon S, Valous NA, Barbin DF (2018) Predicting the ripening of papaya fruit with digital imaging and random forests. Comput Electron Agric 145:76–82. https://doi.org/10.1016/j.compag.2017.12.029

    Article  Google Scholar 

  88. Esehaghbeygi A, Ardforoushan M, Monajemi SAH, Masoumi AA (2010) Digital image processing for quality ranking of saffron peach. Int Agrophys 24(2):115–120. http://www.international-agrophysics.org/digital-image-processing-for-quality-ranking-of-saffron-peach,106361,0,2.html

    Google Scholar 

  89. Shearer S, Payne F (1990) Color and defect sorting of bell peppers using machine vision. Trans ASABE 33:2045–2050

    Google Scholar 

  90. 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(3):339–346. https://doi.org/10.1016/j.jfoodeng.2010.01.010

    Article  Google Scholar 

  91. Liming X, Yanchao Z (2010) Automated strawberry grading system based on image processing. Comput Electron Agric 71:s32–s39. https://doi.org/10.1016/j.compag.2009.09.013

    Article  Google Scholar 

  92. 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 Bioprocess Technol 4(4):487–504. https://doi.org/10.1007/s11947-010-0411-8

    Article  Google Scholar 

  93. Yustika Manik F, Herdiyeni Y, Herliyana E (2016) Leaf morphological feature extraction of digital image anthocephalus cadamba. Telkomnika (Telecommun Comput Electron Control) 14:630. https://doi.org/10.12928/telkomnika.v14i2.2675

    Article  Google Scholar 

  94. Kondo N (2009) Robotization in fruit grading system. Sens Instrum Food Qual Saf 3(1):81–87. https://doi.org/10.1007/s11694-008-9065-x

    Article  Google Scholar 

  95. Van Dalen G (2004) Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Res Int 37(1):51–58. https://doi.org/10.1016/j.foodres.2003.09.001

    Article  Google Scholar 

  96. Zareiforoush H, Minaei S, Alizadeh MR, Banakar A (2016) Qualitative classification of milled rice grains using computer vision and metaheuristic techniques. J Food Sci Technol 53(1):118–131. https://doi.org/10.1007/s13197-015-1947-4

    Article  CAS  PubMed  Google Scholar 

  97. Bharati MH, Liu JJ, Macgregor JF (2004) Image texture analysis: methods and comparisons. Chemom Intell Lab Syst 72(1):57–71. https://doi.org/10.1016/j.chemolab.2004.02.005

    Article  CAS  Google Scholar 

  98. PS SK, VS D (2016) Extraction of texture features using glcm and shape features using connected regions. Int J Eng Technol 8:2926–2930

    Article  Google Scholar 

  99. Lu D, Li G, Moran E, Dutra L, Batistella M (2014) The roles of textural images in improving land-cover classification in the brazilian amazon. Int J Remote Sens 35:8188–8207. https://doi.org/10.1080/01431161.2014.980920

    Article  Google Scholar 

  100. Armi L, Fekri Ershad S (2019) Texture image analysis and texture classification methods - a review 2:1–29

    Google Scholar 

  101. Kavdir I, Guyer DE (2004) Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosys Eng 89(3):331–344. https://doi.org/10.1016/j.biosystemseng.2004.08.008

    Article  Google Scholar 

  102. Bennedsen BS, Peterson DL, Tabb A (2005) Identifying defects in images of rotating apples. Comput Electron Agric 48(2):92–102. https://doi.org/10.1016/j.compag.2005.01.003

    Article  Google Scholar 

  103. Li J, Chen L, Huang W, Wang Q, Zhang B, Tian X, Fan S, Li B (2016) Multispectral detection of skin defects of bi-colored peaches based on vis-nir hyperspectral imaging. Postharvest Biol Technol 112:121–133. https://doi.org/10.1016/j.postharvbio.2015.10.007

    Article  CAS  Google Scholar 

  104. 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(3):535–543. https://doi.org/10.1016/j.jfoodeng.2006.12.007

    Article  Google Scholar 

  105. Li Q, Wang M, Gu W (2002) Computer vision based system for apple surface defect detection. Comput Electron Agric 36(2):215–223. https://doi.org/10.1016/s0168-1699(02)00093-5

    Article  Google Scholar 

  106. Li J, Rao X, Wang F, Wu W, Ying Y (2013b) Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biol Technol 82:59–69. https://doi.org/10.1016/j.postharvbio.2013.02.016

    Article  Google Scholar 

  107. Yang T (1996) Spherical transform of fruit images for on-line defect extraction of mass objects. Opt Eng 35(2):344–350. https://doi.org/10.1117/1.600902

    Article  Google Scholar 

  108. Xiong J, Lin R, Bu R et al (2018) A micro-damage detection method of litchi fruit using hyperspectral imaging technology. Sensors (Switzerland) 18. https://doi.org/10.3390/s18030700

  109. Nturambirwe JFI, Nieuwoudt HH, Perold WJ, Opara UL (2019) Non-destructive measurement of internal quality of apple fruit by a contactless NIR spectrometer with genetic algorithm model optimization. Sci African 3. https://doi.org/10.1016/j.sciaf.2019.e00051

  110. Jiang B, He J, Yang S, Fu H, Li T, Song H, He D (2019) Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artif Intell Agric 1:1–8. https://doi.org/10.1016/j.aiia.2019.02.001

    Article  CAS  Google Scholar 

  111. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics). pp 630–645

  112. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7(September), Article 1419. https://doi.org/10.3389/fpls.2016.01419

  113. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. CVPR 1–14

  114. Zeiler MD, Fergus R (2014) Visualizing and Understanding Convolutional Networks. Computer Vision – ECCV, Cham

  115. Kirk D, Wen-Mei WH (2016) Programming massively parallel processors: a hands-on approach. Morgan Kaufmann

  116. Wu A, Zhu J, Ren T (2020) Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network. Comput Electr Eng 81:106454. https://doi.org/10.1016/j.compeleceng.2019.106454

    Article  Google Scholar 

  117. Fan S, Li J, Zhang Y, Tian X, Wang Q, He X, Zhang C, Huang W (2020) On line detection of defective apples using computer vision system combined with deep learning methods. J Food Eng 286:110102. https://doi.org/10.1016/j.jfoodeng.2020.110102

    Article  Google Scholar 

  118. Ismail N, Malik OA (2021) Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Inf Process Agric. https://doi.org/10.1016/j.inpa.2021.01.005

    Article  Google Scholar 

  119. Yao J, Qi J, Zhang J et al (2021) A real-time detection algorithm for kiwifruit defects based on yolov5. Electron 10(14). https://doi.org/10.3390/electronics10141711

  120. Unay D, Gosselin B (2006) Automatic defect segmentation of “Jonagold” apples on multi-spectral images: A comparative study. Postharvest Biol Technol 42:271–279. https://doi.org/10.1016/j.postharvbio.2006.06.010

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

    Mahmoud Soltani Firouz & Hamed Sardari

Authors
  1. Mahmoud Soltani Firouz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamed Sardari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud Soltani Firouz.

Ethics declarations

Conflict of Interest

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soltani Firouz, M., Sardari, H. Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing. Food Eng Rev 14, 353–379 (2022). https://doi.org/10.1007/s12393-022-09307-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12393-022-09307-1

Keywords

Search

Navigation