Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes

Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-8 Number-8                          
Year of Publication : 2014
Authors : Jaspinder Pal Singh
  10.14445/22315381/IJETT-V8P275

Citation 

Jaspinder Pal Singh. "Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes", International Journal of Engineering Trends and Technology(IJETT), V8(8),438-442 February 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. In our study we are going to design a computer vision algorithm for grading of potatoes according to the greening of the surface colour of potato. The ratio of green pixels to the total number of pixels of the potato surface is found. The higher the ratio the worse is the potato. First the image is converted into serial data and then processing is done in RGB colour space. Green part of the potato is also shown by de-serializing the output. The same algorithm is then synthesized on FPGA and the result shows thousand times speed improvement in case of hardware synthesis.

References

[1] United States Standards for Grades of Potatoes Effective June 3, 2011
[2] Crookes, Danny. "Architectures for high performance image processing: The future." Journal of Systems Architecture 45.10 (1999): 739-748.
[3] Unay, D., Gosselin, B., 2006. Stem and calyx recognition on ‘jonagold’ apples by pattern recognition. ” Journal of Food Engineering “ 78, 597–605.
[4] Bolle, R., Connell, J., Haas, N., Mohan, R., Taubin, G., 1996. Veggievision: a produce recognition system. In: “Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV’96)”. IEEE Computer Society, Washington, DC, USA, p.244
[5]Jelinski, T., jin Du, C., Sun, D.-W., Fornal, J., 2007. Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision. Journal of Food Engineering 83, 3–9.
[6] Munkevik, P., Hall, G., Duckett, T., 2007. A computer vision system for appearancebased descriptive sensory evaluation of meals. Journal of Food Engineering 78, 246–256.
[7] Jarimopas, B., Jaisin, N., 2008. An experimental machine vision system for sorting sweet taramind. Journal of Food Engineering 89, 291–297.
[8] Chen, Yud-Ren, Kuanglin Chao, and Moon S. Kim. "Machine vision technology for agricultural applications." Computers and electronics in Agriculture 36.2 (2002): 173-191.
[9] Marchant, J. A., C. M. Onyango, and M. J. Street. "Computer vision for potato inspection without singulation." Computers and electronics in agriculture 4.3 (1990): 235-244.
[10] Al-Mallahi, A., et al. "Detection of potato tubers using an ultraviolet imaging-based machine vision system." Biosystems Engineering 105.2 (2010): 257-265.
[11] Barnes, Michael, et al. "Visual detection of blemishes in potatoes using minimalist boosted classifiers." Journal of Food Engineering 98.3 (2010): 339-346.
[12] Tao, Y., Morrow, C.T., Heinemann, P.H., 1995b. Fourier-based separation technique for shape grading of potatoes using machine vision. “Transactions of the American Society of Agricultural Engineers” 38 (3), 949–957.
[13] Muir, A.J., Ross, D.W., Dewar, C.J., Kennedy, D., 1999. Defect and disease detection in potato tubers. In: Proceedings of SPIE – The International Society for Optical Engineering, vol. 3543, pp. 199–207.
[14] Tao, Y., Heinemann, P.H., Varghese, Z., 1995a. Machine vision for color inspection of potatoes and apples. “Transactions of the American Society of Agricultural Engineers” 38 (5), 1555–1561.
[15] Heinemann, P.H., Pathare, N.P., Morrow, C.T., 1996. An automated inspection station for machine-cision grading of potatoes.” Machine Vision and Applications” 9 (1), 14–19.
[16] Jin, Jing, et al. "Methodology for potatoes defects detection with computer vision." International Symposium on Information Processing, Huangshan. 2009.
[17] Zhou, L., Chalana, V., Kim, Y., 1998. Pc-based machine vision system for real-time computer-aided potato inspection. “International Journal of Imaging Systems and Technology” 9 (6), 423–433.
[18] Guannan, Z., Yuzhi, T., Junxiong, Z., Wei, L., 2009. Automatic detecting and grading method of potatoes with computer vision. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery 40 (4), 166–168.
[19] Edwards, E.J., Cobb, A.H., 1997. Effect of temperature on glycoalkaloid and chlorophyll accumulation in potatoes (Solanum tuberosum L. cv. King Edward) stored at low photon flux density, including preliminary modeling using an artificial neural network. J. Agric. Food Chem. 45, 1032–1038.
[20] Morris, S.C., Lee, T.H., 1984. The toxicity and teratogenicity of Solanaceae glycoalkaloids, particularly those of the potato (Solanum tuberosum). Food Technol. 36, 118–124.
[21] Kiran, Maleeha, et al. "Implementing image processing algorithms using ‘Hardware in the loop’approach for XILINX FPGA." Electronics design 2008 JCED 2008. International conference on IEEE, 2008.

Keywords
Machine vision, Potato greening, Region of interest, RGB colour space, SIMULINK, HDL Workflow Advisor, USDA, FPGA, Synthesis.