Potential Applications of Computer Vision in Quality Inspection of Rice: A Review

Food Engineering Reviews

Volumn 7, Issue 3, 2015, Pages 321-345

  Zareiforoush, Hemad ^a   Minaei, Saeid ^a   Alizadeh, Mohammad Reza ^b   Banakar, Ahmad ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b RICE RESEARCH INSTITUTE OF IRAN   (Iran)

Author keywords

Computer vision; Image processing; Quality; Rice

Indexed keywords

COST EFFECTIVENESS; IMAGE PROCESSING; IMAGE QUALITY; INTERNATIONAL TRADE;

COMPUTER VISION TECHNIQUES; ECONOMIC ASPECTS; OBJECTIVE QUALITIES; QUALITATIVE FEATURES; QUALITY ASSESSMENT; QUALITY INSPECTION; RICE; TECHNICAL PRINCIPLE;

COMPUTER VISION;

EID: 84940599822     PISSN: 18667910     EISSN: 18667929     Source Type: Journal    
DOI: 10.1007/s12393-014-9101-z     Document Type: Review

References (121)

1. Abud-Archila M, Bonazzi C, Heyd B (1999) Development of an image analysis methodology for following shrinkage and cracking of rice kernels during convective drying. In: 6th conference on food engineering (COFE’99), Dallas, USA
2. Ajay G, Suneel M, Kumar KK, Prasad PS (2013) Quality evaluation of rice grains using morphological methods. Int J Soft Comput Eng 2:35–37
3. Anonymous (2013) Color sorting machines for rice. Promech Industries. http://www.marksorter.com/index.php.
4. Aulakh JS, Banga V (2012) Percentage purity of rice sample by image processing. In: International conference on trends in electrical, electronics and power engineering, Singapore, July 15–16 2012, pp 15–16
5. Bagheri I, Dehpour M, Payman S, Zareiforoush H (2011) Rupture strength of brown rice varieties as affected by moisture content and loading rate. Aust J Crop Sci 5:1239–1246
6. Batchelor BG (1985) Lighting and viewing techniques. In: Batchelor DA, Hill DC (ed) Automated visual inspection. Hodgso Publications Ltd., pp 103–179
7. Brosnan T, Sun D (2004) Improving quality inspection of food products by computer vision—a review. J Food Eng 61:3–16
8. Cardarelli AJ, Tao Y, Bernhardt JL, Lee FN (1999) Nondestructive quantification of internal damage in rough rice caused by insects and fungus. Proc SPIE 3543:111–118
9. Chen X, Ke S, Wang L, Xu H, Chen W (2012) Classification of rice appearance quality based on LS-SVM using machine vision. Information computing and applications. Springer, Berlin, pp 104–109
10. Cheng F, Liu Z, Ying Y (2005) Machine vision analysis of characteristics and image information base construction for hybrid rice seed. Rice Sci 12:13–18
11. Cheng F, Ying Y (2004) Image recognition of diseased rice seeds based on color feature. Proc SPIE 5587:224–232
12. Cheng F, Ying Y, Li Y (2006) Detection of defects in rice seeds using machine vision. Trans ASABE 49:1929–1934
13. Courtois F, Faessel M, Bonazzi C (2010) Assessing breakage and cracks of parboiled rice kernels by image analysis techniques. Food Control 21:567–572
14. Davies ER (2004) Machine vision: theory, algorithms, practicalities. Elsevier, Amsterdam
15. Duan L, Yang W, Huang C, Liu Q (2011) A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice. Plant Methods 7:1–13
16. Faessel M, Courtois F (2009) Touching grain kernels separation by gap-filling. Image Anal Stereol 28:195–203
17. Fang C, Hu X, Sun C, Duan B, Xie L, Zhou P (2014) Simultaneous determination of multi rice quality parameters using image analysis method. Food Anal Methods 8:70–78
18. Fang C, Yi-bin Y (2004) Machine vision inspection of rice seed based on hough transform. J Zhejiang Univ Sci 5:663–667
19. Fant E, Casady W, Goh D, Siebenmorgen T (1994) Grey-scale intensity as a potential measurement for degree of rice milling. J Agric Eng Res 58:89–97
20. Fayyazi S, Abbaspour-Fard MH, Rohani A, Sadrnia H, Monadjemi SA (2013) Identification of three Iranian rice seed varieties in mixed bulks using textural features and learning vector quantization neural network. In: Paper presented at the 1st international e-conference on novel food processing, Mashhad, Iran, 26–27 Feb
21. Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Pearson Prentice Hall, New Jersey
22. Gujjar HS, Siddappa DM (2013) A method for identification of basmati rice grain of india and its quality using pattern classification. Int J Eng Res Applic 3:268–273
23. Gunasekaran S, Ding K (1994) Using computer vision for food quality evaluation. Food Technol 48:151–154
24. Guzman JD, Peralta EK (2008) Classification of Philippine rice grains using machine vision and artificial neural networks. In: World conference on agricultural information and IT, Tokyo, Japan, 24–27 August 2008, pp 41–48
25. Hendriks CLL, Van Vliet LJ (2001) Segmentation-free estimation of length distributions using sieves and RIA morphology. Scale-space and morphology in computer vision. Springer, Berlin, pp 398–406
26. Honda H, Takikawa N, Noguchi H, Hanai T, Kobayashi T (1997) Image analysis associated with a fuzzy neural network and estimation of shoot length of regenerated rice callus. J Ferment Bioeng 84:342–347
27. Huadong Z, Muhua L, Yanhong W, Jing L (2006) Rice fissure detection using computer vision. Trans Chin Soc Agric Eng 22:129–133
28. Ibañez-Carranza AM (2002) A study of the pasting properties of rice flour and starch as affected by rice variety and physicochemical properties. University of California, Davis
29. ISIRI (2012) Rice—specifications and test methods. Institute of Standards and Industrial Research of Iran. http://std.isiri.org/std/127.htm
30. Jackman P, Sun D-W, Allen P (2011) Recent advances in the use of computer vision technology in the quality assessment of fresh meats. Trends Food Sci Technol 22:185–197
31. Jamieson V (2002) Physics raises food standards. Phys World 15:21–22
32. Jayas D, Ghosh P, Paliwal J, Karunakaran C (2007) Quality evaluation of wheat computer vision technology for food quality evaluation. Academic Press, New York, pp 351–376
33. Jinorose M, Prachayawarakorn S, Soponronnarit S (2014) A novel image-analysis based approach to evaluate some physicochemical and cooking properties of rice kernels. J Food Eng 124:184–190
34. Kaláb M, Allan-Wojtas P, Miller SS (1995) Microscopy and other imaging techniques in food structure analysis. Trends Food Sci Technol 6:177–186
35. Kaur H, Singh B (2013) Classification and grading rice using multi-class SVM. Int J Sci Res Public 3:1–5
36. Kim S, Schatzki T (2000) Apple watercore sorting system using X-ray imagery: I. Algorithm development. Trans ASAE 43:1695–1702
37. Kiruthika R, Muruganand S, Periasamy A (2013) Matching of different rice grains using digital image processing international. J Adv Res Electr Electron Instrum Eng 2:2937–2941
38. Koc AB (2007) Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biol Technol 45:366–371
39. Kondo N (2010) Automation on fruit and vegetable grading system and food traceability. Trends Food Sci Technol 21:145–152
40. Krutz GW, Gibson HG, Cassens DL, Min Z (2000) Colour vision in forest and wood engineering. Landwards 55:2–9
41. Lamberts L, Brijs K, Mohamed R, Verhelst N, Delcour JA (2006) Impact of browning reactions and bran pigments on color of parboiled rice. J Agric Food Chem 54:9924–9929
42. Lan Y, Fang Q, Kocher M, Hanna M (2002) Detection of fissures in rice grains using imaging enhancement. Int J Food Prop 5:205–215
43. Leemans V, Magein H, Destain M-F (1998) Defects segmentation on ‘Golden Delicious’ apples by using colour machine vision. Comput Electron Agric 20:117–130
44. Liu W, Tao Y, Siebenmorgen T, Chen H (1998) Digital image analysis method for rapid measurement of rice degree of milling. Cereal Chem 75:380–385
45. Liu Z, Cheng F, Ying Y, Rao X (2005) Identification of rice seed varieties using neural network. J Zhejiang Univ Sci 6:1095
46. Lizhang X, Yaoming L (2009) Detection of stress cracks in rice kernels based on machine vision. Agr Mech Asia Af (AMA) 40:38–41
47. Lloyd B, Cnossen A, Siebenmorgen T (2001) Evaluation of two methods for separating head rice from brokens for head rice yield determination. Appl Eng Agric 17:643–648
48. Lv B, Li B, Chen S, Chen J, Zhu B (2009) Comparison of color techniques to measure the color of parboiled rice. J Cereal Sci 50:262–265
49. Maheshwari CV, Jain KR, Modi CK (2012) Non-destructive quality analysis of Indian Gujarat-17 Oryza sativa SSP Indica (Rice) using image processing. Int J Comput Eng Sci 2:48–54
50. Morita S, Kusuda O, Yonemaru JI, Fukushima A, Nakano H (2005) Effects of topdressing on grain shape and grain damage under high temperature during ripening of rice. Proceedings of the world rice research conference, Tsukuba, Japan 2005:560–562
51. Morita S, Yonemaru JI, Takanashi J (2005) Grain growth and endosperm cell size under high night temperatures in rice (Oryza sativa L.). Ann Bot 95:695–701
52. Mousavi Rad S, Akhlaghian Tab F, Mollazade K (2012) Application of imperialist competitive algorithm for feature selection: a case study on bulk rice classification. Int J Comput Appl 40:41–48
53. Mousavi Rad S, Akhlaghian Tab F, Mollazade K (2012) Design of an expert system for rice kernel identification using optimal morphological features and back propagation neural network. Int J Appl Inf Syst 3:33–37
54. Nagata K, Takita T, Yoshinaga S, Terashima K, Fukuda A (2004) Effect of air temperature during the early grain-filling stage on grain fissuring in rice. Jpn J Crop Sci 73:336–342
55. Narendra V, Hareesha K (2010) Prospects of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation. Int J Comput Appl 1:1–9
56. Nasirahmadi A, Emadi B, Abbaspour-Fard MH, Aghagolzade H (2014) Influence of moisture content, variety and parboiling on milling quality of rice grains. Rice Sci 21:116–122
57. Nixon M, Aguado AS (2008) Feature extraction & image processing. Academic Press, Waltham
58. Novini AR (1995) The latest in vision technology in today’s food and beverage container manufacturing industry. Technical papers. Society of Manufacturing Engineers
59. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11:23–27
60. Papadakis SE, Abdul-Malek S, Kamdem RE, Yam KL (2000) A versatile and inexpensive technique for measuring color of foods. Food Technol 54:48–51
61. Patel KK, Kar A, Jha S, Khan M (2012) Machine vision system: a tool for quality inspection of food and agricultural products. J Food Sci Technol 49:123–141
62. Payman S, Bagheri I, Zareiforoush H (2014) Milling characteristics of rice grains as affected by paddy mixture ratio and moisture content. Int J Biosci 4:87–97
63. Pearson T, Schatzki T (1998) Machine vision system for automated detection of aflatoxin-contaminated pistachios. J Agric Food Chem 46:2248–2252
64. Prajapati BB, Patel S (2013) Algorithmic approach to quality analysis of Indian basmati rice using digital image processing. Int J Emerg Tech Adv Eng 3:503–504
65. Prajapati BB, Patel S (2013) Proposed mobile rice grain analyzer device based on digital image processing with related hardware and software specifications. Am Int J Res Sci Technol Eng Math 13:217–220
66. Razavi SMA (2010) Monitoring geometric characteristics of rice during processing by image analysis system and micrometer measurement. Int Agrophys 24:21–27
67. Rigney M, Brusewitz G, Kranzler G (1992) Asparagus defect inspection with machine vision. Trans ASAE 35:1873–1878
68. Sakai N, Yonekawa S, Matsuzaki A, Morishima H (1996) Two-dimensional image analysis of the shape of rice and its application to separating varieties. J Food Eng 27:397–407
69. Sansomboonsuk S, Afzulpurkar N (2006) The appropriate algorithms of image analysis for rice kernel quality evolution. In: 20th conference of mechanical engineering network of Thailand, Bangkok, Thailand, 18–20 October 2006
70. Sarkar NR (1991) Machine vision for quality control in the food industry. In: Instrumental methods for quality assurance in foods. CRC Press, pp 167–187
71. Shah V, Jain K, Maheshwari CV (2013) Combined parametric evaluation of Kamod rice using artificial neural network. Int J Commun Syst Netw Technol 1:1–6
72. Shantaiya S, Ansari U (2010) Identification of food grains and its quality using pattern classification. In: 12th IEEE international conference on communication technology (ICCT), Nanjing, China, 11–14 November 2010, pp 3–5
73. Shatadal P, Jayas D, Bulley N (1995) Digital image analysis for software separation and classification of touching grains. I: disconnect algorithm. Trans ASAE 38:635–643
74. Shei HJ, Lin CS (2012) An optical automatic measurement method for the moisture content of rough rice using image processing techniques. Comput Electron Agric 85:134–139
75. Shewfelt RL, Bruckner B (2000) Fruit and vegetable quality: an integrated view. CRC Press, Boca Raton
76. Shiddiq DM, Nazaruddin YY, Muchtadi FI, Raharja S (2011) Estimation of rice milling degree using image processing and adaptive network based fuzzy inference system (ANFIS). In: 2nd international conference on instrumentation control and automation (ICA), Bandung, Indonesia, 15–17 Novomber 2011. IEEE, pp 98–103
77. Shimizu N, Haque M, Andersson M, Kimura T (2008) Measurement and fissuring of rice kernels during quasi-moisture sorption by image analysis. J Cereal Sci 48:98–103
78. Shirai Y (1987) Three-dimensional computer vision. Springer, Berlin
79. Siebenmorgen T, Saleh M, Bautista R (2009) Milled rice fissure formation kinetics. Trans ASAE 52:893–900
80. Silva CS, Sonnadara U (2013) Classification of rice grains using neural networks. In: Proceedings of technical sessions, Sri Lanka, September 2013, pp 9–14
81. Soborski M (1995) Machine vision inspection for improved quality and production in the food processing environment. In: Food processing automation IV proceedings of the FPAC conference, St. Joseph, Michigan, USA, 1995
82. Sonka M, Hlavac V, Boyle R (1998) Image processing, analysis, and machine vision. Champion & Hall, PWS Publishing, Boston, USA, pp 2–6
83. Steinmetz V, Roger J, Molto E, Blasco J (1999) On-line fusion of colour camera and spectrophotometer for sugar content prediction of apples. J Agric Eng Res 73:207–216
84. Stroshine RL (2004) Physical properties of agricultural materials and food products. R. Stroshine
85. Sun DW (2000) Inspecting pizza topping percentage and distribution by a computer vision method. J Food Eng 44:245–249
86. Sun DW (2011) Computer vision technology for food quality evaluation. Academic Press, Waltham
87. Suzuki T, Chiba A, Yarno T (1997) Interpretation of small angle X-ray scattering from starch on the basis of fractals. Carbohydr Polym 34:357–363
88. Tajima R, Kato Y (2011) Comparison of threshold algorithms for automatic image processing of rice roots using freeware ImageJ. Field Crops Res 121:460–463
89. Talbot H, Appleton B (2002) Elliptical distance transforms and the object splitting problem. In: Mathematical morphology. Proceedings of the 6th international symposium (ISMM), Australia, pp 229–240
90. Tashiro T, Wardlaw I (1991) The effect of high temperature on kernel dimensions and the type and occurrence of kernel damage in rice. Crop Pasture Sci 42:485–496
91. Tated K, Morade S (2012) Application of image processing for automatic cleaning of rice. In: Paper presented at the 1st international conference on recent trends in engineering & technology, Nashik, India, 24–25th March
92. Teoh C, Bakar BA (2009) Immature paddy quantity determination using image processing and analysis techniques. J Trop Agric Food Sci 37:241–248
93. Thomson M et al (2003) Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor Appl Genet 107:479–493
94. Timmermans A (1995) Computer vision system for on-line sorting of pot plants based on learning techniques. II international symposium on sensors in horticulture 421:91–98
95. USDA (2009) United States standards for rice. United States Department of Agriculture Washington, DC, USA. http://www.gipsa.usda.gov/fgis/standproc/usstands.html
96. 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 37:51–58
97. Verma B (2010) Image processing techniques for grading & classification of rice. In: International conference on computer and communication technology (ICCCT), Allahabad, Uttar Pradesh, India, 17–19 September 2010. IEEE, pp 220–223
98. Wallin P, Haycock P (1998) Foreign body prevention, detection and control. Blackie Academic & Professional, London
99. Wan Y, Lin C, Chiou J (2002) Rice quality classification using an automatic grain quality inspection system. Trans ASAE 45:379–387
100. Wan P, Long C (2011) An inspection method of rice milling degree based on machine vision and gray-gradient co-occurrence matrix. In: Computer and computing technologies in agriculture IV. Springer, pp 195–202
101. Wang W, Paliwal J (2006) Separation and identification of touching kernels and dockage components in digital images. Can Biosyst Eng 48:7
102. Wang Y, Gao H, Liang Y (2011) Parametric detection of rice kernel shape using machine vision. Sens Lett 9:1212–1219
103. Weber GF, Menko AS (2005) Color image acquisition using a monochrome camera and standard fluorescence filter cubes. Biotechniques 38:52, 54, 56
104. Wu Y, Lin Q, Chen Z, Wu W, Xiao H (2012) Fractal analysis of the retrogradation of rice starch by digital image processing. J Food Eng 109:182–187
105. Xiaopeng D, Dahong X (2010) Research on conjoint grains of rice based on machine vision. In: 2nd international conference on signal processing systems (ICSPS), Dalian, China, 5–7 July 2010. IEEE, pp V2-760–V762-763
106. Yadav B, Jindal V (2001) Monitoring milling quality of rice by image analysis. Comput Electron Agric 33:19–33
107. Yadav B, Jindal V (2007) Dimensional changes in milled rice (Oryza sativa L.) kernel during cooking in relation to its physicochemical properties by image analysis. J Food Eng 81:710–720
108. Yadav B, Jindal V (2007) Modeling changes in milled rice (Oryza sativa L.) kernel dimensions during soaking by image analysis. J Food Eng 80:359–369
109. Yadav B, Jindal V (2008) Changes in head rice yield and whiteness during milling of rough rice (Oryza sativa L.). J Food Eng 86:113–121
110. Yan L, Lee SR, Yang SH, Lee CY (2010) CCD rice grain positioning algorithm for color sorter with line CCD camera. Online J Power Energy Eng 1:125–129
111. Yang Q (1994) An approach to apple surface feature detection by machine vision. Comput Electron Agric 11:249–264
112. Yang Q (1996) Apple stem and calyx identification with machine vision. J Agric Eng Res 63:229–236
113. Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol
114. Yao M, Liu M, Zheng H (2010) Exterior quality inspection of rice based on computer vision. In: World automation congress (WAC), Kobe, Japan, 19–23 September 2010. IEEE, pp 369–374
115. Yao Q, Chen J, Guan Z, Sun C, Zhu Z (2009) Inspection of rice appearance quality using machine vision. In: WRI global congress on intelligent systems, Xiamen, China, 19–21 May 2009. IEEE, pp 274–279
116. Ying Y, Jing H, Tao Y, Zhang N (2003) Detecting stem and shape of pears using Fourier transformation and an artificial neural network. Trans ASAE 46:157–162
117. Yonemaru JI, Morita S (2012) Image analysis of grain shape to evaluate the effects of high temperatures on grain filling of rice. Field Crops Res 137:268–271
118. Yoshioka Y, Iwata H, Tabata M, Ninomiya S, Ohsawa R (2007) Chalkiness in rice: potential for evaluation with image analysis. Crop Sci 47:2113–2120
119. Zareiforoush H, Komarizadeh M, Alizadeh M (2009) Effect of moisture content on some physical properties of paddy grains. Res J Appl Sci Eng Technol 1:132–139
120. Zareiforoush H, Komarizadeh M, Alizadeh M (2010) Effects of crop-machine variables on paddy grain damage during handling with an inclined screw auger. Biosys Eng 106:234–242
121. Zheng C, Sun DW, Zheng L (2006) Recent applications of image texture for evaluation of food qualities—a review. Trends Food Sci Technol 17:113–128