Skip to main content
Log in

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

  • Review Article
  • Published:
Food Engineering Reviews Aims and scope Submit manuscript

Abstract

Among the cereals, rice is the major foodstuff for a large part of the world’s population. Due to its tremendous importance in the global market, its qualitative economic aspects during processing have always been attended by producers. As the most delicate of the cereals, rice needs the utmost care during post-harvest handling and processing, because in most cases, it is consumed as whole kernel. The growing demand for production of rice with high-quality and safety standards has increased the need for its accurate, fast and objective quality monitoring. Computer vision techniques, as novel technologies, can provide an automated, nondestructive and cost-effective way to achieve these requirements. In recent years, various studies have been conducted to evaluate rice qualitative features based on computer vision techniques. This paper presents the theoretical and technical principles of computer vision for nondestructive quality assessment of rice combined with a review of the recent achievements and applications for quality inspection and monitoring of the product.

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.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Google Scholar 

  11. Cheng F, Ying Y (2004) Image recognition of diseased rice seeds based on color feature. Proc SPIE 5587:224–232

    Article  Google Scholar 

  12. Cheng F, Ying Y, Li Y (2006) Detection of defects in rice seeds using machine vision. Trans ASABE 49:1929–1934

    Article  Google Scholar 

  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

    Article  Google Scholar 

  14. Davies ER (2004) Machine vision: theory, algorithms, practicalities. Elsevier, Amsterdam

    Google Scholar 

  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

    Article  Google Scholar 

  16. Faessel M, Courtois F (2009) Touching grain kernels separation by gap-filling. Image Anal Stereol 28:195–203

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  23. Gunasekaran S, Ding K (1994) Using computer vision for food quality evaluation. Food Technol 48:151–154

    Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Article  CAS  Google Scholar 

  27. Huadong Z, Muhua L, Yanhong W, Jing L (2006) Rice fissure detection using computer vision. Trans Chin Soc Agric Eng 22:129–133

    Google Scholar 

  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

    Google Scholar 

  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

    Article  CAS  Google Scholar 

  31. Jamieson V (2002) Physics raises food standards. Phys World 15:21–22

    Google Scholar 

  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

    Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  35. Kaur H, Singh B (2013) Classification and grading rice using multi-class SVM. Int J Sci Res Public 3:1–5

    Google Scholar 

  36. Kim S, Schatzki T (2000) Apple watercore sorting system using X-ray imagery: I. Algorithm development. Trans ASAE 43:1695–1702

    Article  Google Scholar 

  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

    Google Scholar 

  38. Koc AB (2007) Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biol Technol 45:366–371

    Article  Google Scholar 

  39. Kondo N (2010) Automation on fruit and vegetable grading system and food traceability. Trends Food Sci Technol 21:145–152

    Article  CAS  Google Scholar 

  40. Krutz GW, Gibson HG, Cassens DL, Min Z (2000) Colour vision in forest and wood engineering. Landwards 55:2–9

    Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  45. Liu Z, Cheng F, Ying Y, Rao X (2005) Identification of rice seed varieties using neural network. J Zhejiang Univ Sci 6:1095

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  57. Nixon M, Aguado AS (2008) Feature extraction & image processing. Academic Press, Waltham

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  63. Pearson T, Schatzki T (1998) Machine vision system for automated detection of aflatoxin-contaminated pistachios. J Agric Food Chem 46:2248–2252

    Article  CAS  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  66. Razavi SMA (2010) Monitoring geometric characteristics of rice during processing by image analysis system and micrometer measurement. Int Agrophys 24:21–27

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  75. Shewfelt RL, Bruckner B (2000) Fruit and vegetable quality: an integrated view. CRC Press, Boca Raton

    Google Scholar 

  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

    Article  Google Scholar 

  78. Shirai Y (1987) Three-dimensional computer vision. Springer, Berlin

    Book  Google Scholar 

  79. Siebenmorgen T, Saleh M, Bautista R (2009) Milled rice fissure formation kinetics. Trans ASAE 52:893–900

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  86. Sun DW (2011) Computer vision technology for food quality evaluation. Academic Press, Waltham

    Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  99. Wan Y, Lin C, Chiou J (2002) Rice quality classification using an automatic grain quality inspection system. Trans ASAE 45:379–387

    Google Scholar 

  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

    Article  Google Scholar 

  102. Wang Y, Gao H, Liang Y (2011) Parametric detection of rice kernel shape using machine vision. Sens Lett 9:1212–1219

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Google Scholar 

  111. Yang Q (1994) An approach to apple surface feature detection by machine vision. Comput Electron Agric 11:249–264

    Article  Google Scholar 

  112. Yang Q (1996) Apple stem and calyx identification with machine vision. J Agric Eng Res 63:229–236

    Article  Google Scholar 

  113. Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saeid Minaei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zareiforoush, H., Minaei, S., Alizadeh, M.R. et al. Potential Applications of Computer Vision in Quality Inspection of Rice: A Review. Food Eng Rev 7, 321–345 (2015). https://doi.org/10.1007/s12393-014-9101-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12393-014-9101-z

Keywords

Navigation