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2014 | 17 | 4 |

Tytuł artykułu

Computer vision system to estimate cashew kernel (white wholes) grade geometric and colour parameters

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN

Wydawca

-

Rocznik

Tom

17

Numer

4

Opis fizyczny

http://www.ejpau.media.pl/volume17/issue4/art-05.html

Twórcy

  • Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal University, India
  • Department of Computer Applications, Manipal Institute of Technology, Manipal University, India

Bibliografia

  • 1. Ashok Kumar J., Rao P.R., Desai A.R., 2013. Cashew Kernel Classification using Machine Learning Approaches. Journal of the Indian Society of Agricultural Statistics, 67(1), 121–129.
  • 2. Balasubramanian D., 2001. Physical properties of raw Cashew nut. Journal of agricultural Engineering and Research, 78(3), 291–297.
  • 3. Bhat M.G., Yadukumar N., Gangadhara Nayak M., 2007. Cashew research achivements, technologies developed and research strategies. Natinal Seminar on Research, Development and Marketing of Cashew, Souvenier and Extended Summaries, ICAR Research Complex for Goa.
  • 4. Brosnan T., Sun D.W., 2004, Improving quality inspection of food products by computer vision: a review. J. Food Engineering 61, 3–16.
  • 5. Cashew Export Promotion Council of India, CEPCI:2013. http://www.Cashewindia.org and http://www.cepci.org (online reference).
  • 6. Deddy Wirawan Soedibyo, Usman Ahmad, Kudang Boro Seminar, Dewa Made Subrata I., 2010. The Development of Automatic Coffee sorting system based on image processing and artificial neural network. The International Conference on the quality information for competitive agricultural based production system and commerce, 272–275.
  • 7. Du C.J., Sun D.W., 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Transaction of Food Sci. Tech., 15, 230–249.
  • 8. Du C.J., Sun D.W., 2005. Comparison of three methods for classification of pizza topping using different colour space transformations. J. Food Engg., 68, 277–287.
  • 9. Femat-Diaz A., Vargas-Vazquez D., Huerta-Manzanilla E., Rico-Garcia E. and Herrera-Ruiz G., 2011. Scanner image methodology (SIM) to measure dimensions of leaves for agronomical applications. African Journal of Biotechnology, 10(10), 1840–1847.
  • 10. Gonzalez R., Woods R.E., 2002. Digital Image Processing. 2 ed. Prentice Hall Press.
  • 11. Hanbury A., 2002. The taming of the hue, saturation, and brightness colour space. In CVWW’02-Computer Vision Winter Workshop, 234–243.
  • 12. Hong Chen, Jing Wang, Qiaoxia Yuan, Peng Wan, 2011. Quality classification of peanuts based on image processing. Journal of Food, Agriculture and Environment, 9(3&4), 205–209.
  • 13. Jain A.K., 1989. Fundamentals of Digital Image Processing. Englewood Cliffs: Prentice Hall.
  • 14. Mahesh Kumar, Ganesh Bora, Dongqing Lin, 2013. Image Processing technique to estimate geometric parameters and volume of selected dry beans. Journal of Food Measurement and Characterization, 7(2), 81–89.
  • 15. Mayur Thakkar, Malay Bhatt, Bhensdadia C.K., 2011. Performance Evaluation of Classification Techniques for Computer Vision based Cashew Grading System. International Journal of Computer Applications, 18(6), 9–12.
  • 16. Mendoza F., Dejmek P., Aguilera J. M., 2006. Calibrated colour measurements of agricultural foods using image analysis, Post Bio. and Tech., 41, 285–295.
  • 17. Narendra V.G., Dasharathraj K.S., Hareesh K.S., 2012. Computer Vision System for Cashew Kernel Area Estimation. International Conference on Computing Communication and Networking Technologies (ICCCNT 2012), IEEE Conference Publications, 1–6.
  • 18. Narendra V.G., Hareesh K.S., 2011. Cashew Kernels Classification using Colour Features. International Journal of Machine Intelligence, 3(2), 52–57.
  • 19. Pearson T., Toyofuku N., 2000. Automated sorting of pistachio nuts with closed shells. Applied Engineering in Agriculture, 16, 91–94.
  • 20. Pedreschi F., Leon J., Mery D., Moyono P., 2006. Development of a Computer Vision system to measure the colour of potato chips. Food Research International, 39, 1092–1098.
  • 21. Rafael Namias, Carina Gallo, Roque M. Craviotto, Miriam R. Arango, Pablo M. Granitto, 2012. Automatic Grading of Green Intensity in Soybean Seeds. 13th Argentine Symposium on Artificial Intelligence, ASAI, 96–104.
  • 22. Saroj P.L., Balasubramanian D., 2013. Cashew Industry in India-a sustainable road map. Indian Horticultue, 58(1), 9–15.
  • 23. Sasi Varma K., 2007. Marketing Strategies for Cashews. National Seminar on Research, Development and Marketing of Cashew, Sounier and Extended Summaries, ICAR Research Complex for Goa, 112–115.
  • 24. Sonia Castelo-Quispe, July Diana Banda-Tapia, Monika N., Lopez-Paredes, Dennis Barrios-Aranibar, Patino-Escarcina, 2013. Optimization of Brazil-nuts classification process through automation using colour spaces in computer vision. Int. Journal of Computer Information Systems and Industrial Management Applications, 5, 623–630.
  • 25. Stokes M., Anderson M., Chandrasekar S., Motta R., 1996. A Standard default colour space for the internet sRGB, Version 1.10. International Colour Consortium (ICC), 1899 Preston White Drive, Reston,V.A.
  • 26. Sun D.W., Brosnan T., 2003a. Pizza quality evaluation using computer – Part 2: Pizza topping analysis. Journal of Food Engineering, 57, 91–95.
  • 27. Sun D.W., Brosnan T., 2003b. Pizza quality evaluation using computer – Part 1: Pizza base and sauce spread. Journal of Food Engineering, 57, 81–89.
  • 28. Sun D.W., 2008. Computer Vision Technology for Food Quality Evaluation. Food Science.
  • 29. Suzanne Nielsen S., 2003. Food Analysis. 3rd Edition, Kluwer Academic, Plenum Publishers, 529–541.
  • 30. Tkalcic M., Tasic J.F., 2003. Colour spaces: perceptual, historical and application backround. Computer as a tool the IEEE Region, 8 (1), 304–308.
  • 31. Zheng C., Sun D.W., Zheng L., 2006b. Recent developments and applications of image features for food quality evaluation and inspection- a review. Trends in Food Science and Technology, 17, 642–655.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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