Abstract
This paper describes a new apple classification system based on machine vision and artificial neural network (ANN), which classifies apple in real time on the basis of physical parameters of apple such as size, color and external defects. A specific hardware subsystem has been developed and described for every stage of input and output. The hardware subsystem is interfaced with the software to make the whole system automatic. The purpose of this paper is to automate apple classification. Presently, ANN is used in a wide range of classification applications. We have trained a back-propagation neural network to classify apple. Two sets of variables are used for the training purpose. First set is the independent variable, which is the surface level apple quality parameter. Second set is the dependent variable, which is the quality of the apple. The results of ANN model are discussed; however, the modeling results showed that there is an excellent agreement between the experimental data and predicted values, with a high determination coefficient, very good performance, fewer parameters, shorter calculation time and lower prediction error. The classification accuracy achieved is high, showing that a neural network is capable of making such classification. A low level of errors in classification confirmed that the neural network models are an effective instrument for apple classification. This model might be an alternative method for assessing the quality of apple and provide consumers with a safer food supply.
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References
Alchanatis V, Peleg K, Ziv M (1993) Classification of tissue culture segments by colour machine vision. JAER 55:299–311
Basu JK, Bhattacharyya D, Kim T (2010) Use of artificial neural network in pattern recognition. Int J Softw Eng Appl 4(2):23–32
Bhatt AK, Pant D, Singh R (2009) An analysis of the performance of artificial neural network technique for apple classification. AI & Soc J Knowl Culture Commun 24(1). doi:10.1007/s00146-012-0425-z
Brosnan T, Sun DW (2004) Improving quality inspection of food products by computer vision: a review. J Food Eng 61:3–16
Dickson MA, Bausch WC, Howarth MS (1994) Classification of a broadleaf weed, a grassy weed, and corn using image processing techniques. SPIE 2345:297–305
Edwards EJ, Cobb AH (1999) The effect of prior storage on the potential of potato tubers (Solanum tuberosum L.) to accumulate glycoalkaloids and chlorophylls during light exposure, including artificial neural network modelling. J Sci Food Agric 79:1289–1297
Farkas I, Remenyi P, Biro A (2000) A neural network topology for modelling grain drying. Comp Electron Agric 26:147–158
Gerrard DE, Gao X, Tan J (1996) Beef marbling and color score determination by image processing. J Food Sci 61:145–148
Greenwood CS, Chamberlin DW, Gatos L (1973) Apparatus for sorting fruit according to Color, Patent no. 3,770,111, Calif
Growe TG, Delwiche MJ (1996) A system for fruit defect detection in real-time. AGENG 96, Paper No. 96F-023
Guyer DE, Miles GE, Gaultney LD, Schereiber MM (1993) Application of machine vision to shape analysis in leaf and plant identification. TASAE 36(1):163–171
Hussian MA, Shaur M, Ng NW (2002) Predictions of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. J Food Eng 51:239–248
Jamieson V (2002) Physics raises food standards. Phys World 1:21–22
Leemans V, Magein H, Destain MF (1998) Defect segmentation on ‘golden delicious’ apples by using colour machine vision. Comput Electron Agri 20(2):117–130
Leemans V, Magein H, Destain MF (1999) Defect segmentation on ‘jonagold’ apples using colour vision and a bayesian classification method. Comput Electron Agri 23(1):43–53
Liao K, Paulsen MR, Reid JF (1993) Corn kernel breakage classification by machine vision using a neural network classifier. Trans ASAE 36(6):1949–1953
Mathworks Online (2009) Available: http://www.mathworks.com
McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133
McDonald T, Chen YR (1990) Separating connected muscle tissues in images of beef carcass rib eyes. Trans ASAE 33:2059–2065
Miller BK, Delwiche MJ (1991) Peach defect detection with machine vision. TASAE 34(6):2588–2597
Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533
Moltó E, Aleixos N, Ruiz LA, Vázquez J, Juste F (1996), An artificial vision system for fruit quality assessment. AGENG 96, Madrid, Paper No. 96F-078
Ni H, Gunasekaran S (1998) Food quality prediction with neural networks. Food Technol 5:60–65
Okamura NK, Delwiche MJ, Thompson JF (1991) Raising grading by machine vision. ASAE Paper No. 91-7011. St. Joseph, MI
Rigeny MP, Kranzler GA (1989) Seedling classification performance of a neural network, ASAE paper 89-7523, ASAE, St. Joseph, MI 49085-9659
Sarkar N, Wolfe RR (1985) Feature extraction techniques for sorting tomatoes by computer vision. Trans ASAE 28(3):970–979
Sayeed MS, Whittaker AD, Kehtarnavaz ND (1995) Snack quality evaluation method based on image feature and neural network prediction. Trans ASAE 38(4):1234–1245
Sofu A, Ekinci FY (2007) Estimation of storage time of yogurt with artificial neural network modeling. J Dairy Sci 90:3118–3125. doi:10.3168/jds.2006-591
Sun DW (2004) Computer vision: an objective, rapid and noncontact quality evaluation tool for the food industry. J Food Eng 61:1–2
Suzuki ST, Uesugi YM, Sekinem YM (1977) Automatic sorting conveyor systems. Patent no. 4,031,998, Japan
Tao Y, Morrow CT, Heinemann PH, Sommer JH (1990), Automated machine vision inspection of potatoes. ASAE Paper No. 90-3531
Tengsater TN, Park T (1977) Apparatus for measuring the internal quality of produce. Patent no. 4,035,636, Md
Varghese Z, Morrow CT, Heinemann PH, Sommer HJ, Tao Y, Crassweller RM (1991). Automated inspection of Golden Delicious apples using colour computer vision. ASAE Paper No. 91-7002
Wang HH, Sun DW (2002) Assessment of cheese browning affected by baking conditions using computer vision. J Food Eng 56:339–345
Zakaria Z, Isa NAM, Suandi SA (2010) A study on neural network training algorithm for multiface detection in static images. World Acad Sci Eng Technol 62
Zweiri YH, Whidborne JF, Sceviratne LD (2002) A three-term backpropagation algorithm. Neurocomputing 50:305–318
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This research work was fully supported by Department of Science and Technology (science and society division), Government of India, under the scheme for Young Scientist and Professional.
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Bhatt, A.K., Pant, D. Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI & Soc 30, 45–56 (2015). https://doi.org/10.1007/s00146-013-0516-5
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DOI: https://doi.org/10.1007/s00146-013-0516-5