High speed intelligent classifier of tomatoes by colour, size and weight
Abstract
At present most horticultural products are classified and marketed according to quality standards, which provide a common language for growers, packers, buyers and consumers. The standardisation of both product and packaging enables greater speed and efficiency in management and marketing. Of all the vegetables grown in greenhouses, tomatoes are predominant in both surface area and tons produced. This paper will present the development and evaluation of a low investment classification system of tomatoes with these objectives: to put it at the service of producing farms and to classify for trading standards. An intelligent classifier of tomatoes has been developed by weight, diameter and colour. This system has optimised the necessary algorithms for data processing in the case of tomatoes, so that productivity is greatly increased, with the use of less expensive and lower performance electronics. The prototype is able to achieve very high speed classification, 12.5 ratings per second, using accessible and low cost commercial equipment for this. It decreases fourfold the manual sorting time and is not sensitive to the variety of tomato classified. This system facilitates the processes of standardisation and quality control, increases the competitiveness of tomato farms and impacts positively on profitability. The automatic classification system described in this work represents a contribution from the economic point of view, as it is profitable for a farm in the short term (less than six months), while the existing systems, can only be used in large trading centers.Downloads
References
Agugliaro FM, 2007. The gasification of vegetable residues of greenhouses for making electrical energy in the south of Spain: his ubication study by GIS. Interciencia 32(2): 131-136.
Alfatni MS, Shariff ARM, Abdullah MZ, Ben Saeed OM, Ceesay OM, 2011. Recent methods and techniques of external grading systems for agricultural crops quality inspection – Review. Int J Food Eng 7(3): art. no. 10. doi:10.2202/1556-3758.1932. http://dx.doi.org/10.2202/1556-3758.1932
Asadollahi H, Kamarposhty MS, Teymoori MM, 2009. Classification and evaluation of tomato images using several classifier. Computer Science and Information Technology - Spring Conf IACSITSC '09, pp. 471-474.
Butterworth HM, Butterworth WR, 1979. Electronic weighing and recording system for tomato grading. ASAE-CSAE Summer Meeting, Winnipeg, Manitoba, Canada. June 24-27, pp: 4-8.
Callejon-Ferre AJ, Manzano-Agugliaro F, Diaz-Perez M, Carreño-Ortega A, Perez-Alonso J, 2009. Effect of shading with aluminised screens on fruit production and quality in tomato (Solanum lycopersicum L.) under greenhouse conditions. Span J Agric Res 7(1): 41-49.
Callejon-Ferre AJ, Velazquez-Marti B, Lopez-Martinez JA, Manzano-Agugliaro F, 2011. Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value. Renew Sust Energ Rev 15(2): 948–955. http://dx.doi.org/10.1016/j.rser.2010.11.012
Choi K, Lee G, Han YJ, Bunn JM, 1995. Tomato maturity evaluation using colour image analysis. T Am Soc Agric Eng 38(1): 171–176.
Costa C, Antonucci F, Pallotino F, Aguzzi J, Sun D-W, Menesatti P, 2011. Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food Bioprocess Technol 4(5): 673-692. http://dx.doi.org/10.1007/s11947-011-0556-0
Fernandes C, Corá JE, Braz LT, 2007. Grading of cherry tomatoes based on fruit size and weight. Horticultura Brasileira 25(2), 275-278. http://dx.doi.org/10.1590/S0102-05362007000200029
Frances JV, Calpe J, Martinez M, Rosado A, Serrano, AJ, Calleja J, Diaz M, 2000. Application of ARMA modeling to the improvement of weight estimations in fruit sorting and grading machinery Acoustics, Speech, and Signal Processing. Proc 2000 IEEE Int Conf, Vol 6. pp: 3666-3669.
Gunasekaran S, Ding K, 1994. Using computer vision for food quality evaluation. Food Technol 6: 151-154.
Hauch and Bach, 2011. Load cell digitizing unit LDU78.1. Available online in http://www.haubac.com/haubac.asp?p1=54 [15 February 2011].
Hong T, Qiao J, Ning W, Ngadi MO, Zhao Z, Li Z, 2007. Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique. T Chinese Soc Agric Eng 23(2): 151-155.
Jahns G, Moller-Nielsen H, Wolfgang P, 2001. Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading. Comput Electron Agr 31(1), 17-29. http://dx.doi.org/10.1016/S0168-1699(00)00171-X
Laykin S, Alchanatis V, Fallik E, Edan Y, 2002. Image processing algorithms for tomato classification. T ASAE 45(3): 851-858.
Leemans V, Destain MF, 2004. A real-time grading method of apples based on features extracted from defects. J Food Engg 61: 83–89. http://dx.doi.org/10.1016/S0260-8774(03)00189-4
Leemans V, Magein H, Destain MF, 1998. Defects segmentation on ‘golden delicious’ apples by using colour machine vision. Comput Electron Agr 20: 117–130. http://dx.doi.org/10.1016/S0168-1699(98)00012-X
Leemans V, Magein H, Destain MF, 1999. Defect segmentation on ‘jonagold’ apples using colour vision and a bayesian classification method. Comput Electron Agr 23: 43–53. http://dx.doi.org/10.1016/S0168-1699(99)00006-X
Lino ACL, Sanches J, Dal Fabbro IM, 2008. Image processing techniques for lemons and tomatoes classification. Bragantia 67(3): 785-789. http://dx.doi.org/10.1590/S0006-87052008000300029
Manzano-Agugliaro F, Cañero-Leon R, 2010. Economics and environmental analysis of Mediterranean greenhouse crops. Afr J Agric Res 5(22), 3009–3016.
Marquez AL, Baños R, Gil C, Montoya MG, Manzano-Agugliaro F, Montoya FG, 2011. Multi-objective crop planning using pareto-based evolutionary algorithms. Agr Econ 42(6): 649-656. http://dx.doi.org/10.1111/j.1574-0862.2011.00546.x
Mendoza F, Aguilera JM, 2004. Application of image analysis for classification of ripening bananas. J Food Sci 69(9): 471-477. http://dx.doi.org/10.1111/j.1365-2621.2004.tb09932.x
Mery D, Pedreschi F, 2005. Segmentation of colour food images using a robust algorithm. J Food Eng 66: 353–360. http://dx.doi.org/10.1016/j.jfoodeng.2004.04.001
Meyers JB, 1988 Improving dynamic visual inspection performance, M.Sc. thesis, Univ. of Georgia, Athens.
Miller BK, Delwiche MJ, 1989. A color vision system for peach grading. T ASAE 34(6): 2509-2515.
Mitsubishi, 2011. PLCs Modulares – Control PLC. Q Series. Available online in http://www.mitsubishi-automation.es [15 February 2011].
Moltó E, Blasco J, Benlloch JV, 1998. Computer vision for automatic inspection of agricultural produces. SPIE Symp on Precision Agriculture and Biological Quality. 1–6 November, Boston, MA, USA.
Morimoto T, Takeuchi T, Miyata H, Hashimoto Y, 2000. Pattern recognition of fruit shape based on the concept of chaos and neural networks. Comput Electron Agr 26: 171–186. http://dx.doi.org/10.1016/S0168-1699(00)00070-3
Novini A, 1993. Fundamentals of machine vision lighting. Proc WESCON '93. San Francisco, CA, USA, Sept 28-30. pp 44-52. Doi 10.1109/WESCON.1993.488407. http://dx.doi.org/10.1109/WESCON.1993.488407
OIML, 2000. Recommendation of the International Organisation of Legal Metrology, R 60 Metrological regulation for load cells. Edition: 2000.
O’Brien M, Garret RE, 1984. Development of a mechanical and electronic assisted tomato grading station. T Am Soc Agr Eng 27(5): 1614-1618.
OJ, 2009. Establishing the standard import values for determining the entry price of certain fruit and vegetables. Commission Regulation (EC) No 771/2009 of 25 August 2009 amending Regulation (EC) No 1580/2007.
Pencue-Fierro L, Patiño-Velasco M, Zambrano-Velasco C, Leon-Tellez J, 2004. Digital image analysis in the design and implementation of a visual inspection system for fruit grading. Proc of SPIE - The International Society for Optical Engineering 5622 (Part 1), art. no. 17, pp. 91-96.
Rocha L, Velho L, Carvalho PCP, 2004. Motion reconstruction using moments analysis. Proc 17th Brazilian Symp on Computer Graphics and Image Processing. pp: 354-361. http://dx.doi.org/10.1109/SIBGRA.2004.1352981
Sarkar N, Wolfe RR, 1985. Feature extraction techniques for sorting tomatoes by computer vision. T ASAE 28(3): 970–974.
Sebestyen G, Nicoara D, Rusu M, Buzdugan L, Cerghizan M, Saplacan G, 2008. supervisory control and quality assurance in food industry. AQTR 2008, IEEE Int Conf on Automation, Quality and Testing, Robotics, vol 1, pp: 195-199.
Sick, 2011. Sensor intelligence. Rotary incremental encoders. Available online in http://www.sick.com/group/en/home/products/product_portfolio/encoders/pages/rotary_incremental.aspx. [15 February 2011].
Singh N, Delwiche MJ, Johnson RS, 1993. Image analysis methods for real-time color grading of stonefruit. Comput Electron Agr 9(1): 71-84. http://dx.doi.org/10.1016/0168-1699(93)90030-5
Smith NE, O’Brien M, 1979. Electronic weighing and recording system for tomato grading. ASAE-CSAE Summer Meeting. Winnipeg, Manitoba, Canada, June 24-27, pp: 20-40.
Syahrir WM, Suryanti A, Connsynn C, 2009. Color grading in tomato maturity estimator using image processing technique. Proc 2nd IEEE Int Conf on Computer Science and Information Technology, art. no. 5234497, pp: 276-280.
USDA, 1991. United States standards for grades of fresh tomatoes. USDA Agricultural Marketing Service, Washington, DC.
Utilcell, 2011. Células de carga monocélula. Modelo 260. Available online in http://www.utilcell.es/secciones/productos/ficha.aspx?seccio=productos&vista=0&categoria=0&tipus=0&model=modelo_260. [15 February 2011].
Zheng C, Sun D-W, Zheng L. 2006. Recent developments and applications of image features for food quality evaluation and inspection - a review. Trends in Food Science and Technology 17 (12): 642-655. http://dx.doi.org/10.1016/j.tifs.2006.06.005 PMid:21299575
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