International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 6, No. 10, 8 Sep. 2014
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A Neural Network Based Recognition and Classification of Commonly Used Indian Non Leafy Vegetables
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Author(s)
Index Terms
Machine vision, Digital image processing, Neural Networks classifier, Color features, Vegetable Recognition, Agricultural/horticultural produce
Abstract
A methodology to characterize the commonly used Indian non-leafy vegetables’ images is developed. From the captured images of Indian non-leafy vegetables, color components, namely, RGB and HSV features are extracted, analyzed and classified. A feed forward backpropagation artificial neural network (BPNN) is used for the classification. The results show that it has good robustness and a very high success rate in the range of 96-100% for eight types of vegetables. The work finds usefulness in developing recognition system for super market, automatic vending, packing and grading of vegetables, food preparation and Agriculture Produce Market Committee (APMC).
Cite This Paper
Ajit Danti, Manohar Madgi, Basavaraj S. Anami,"A Neural Network Based Recognition and Classification of Commonly Used Indian Non Leafy Vegetables", IJIGSP, vol.6, no.10, pp. 62-68, 2014.DOI: 10.5815/ijigsp.2014.10.08
Reference
[1]Ajit Danti, Manohar Madgi & Basavaraj S. Anami, “Mean and Range Color Features Based Identification of Common Indian Leafy Vegetables”. In: International Journal of Signal Processing, Image Processing and Pattern Recognition, 2012, 5(3): 151-160.
[2]Anderson Rocha, Daniel C. Hauagge, Jacques Wainer & Siome Goldenstein, “Automatic fruit and vegetable classification from images”. In: Computers and Electronics in Agriculture 70, 2010: 96-104.
[3]Basavaraj S. Anami, Suvarna S. Nandyal, A. Govardhan, “A Combined Color, Texture and Edge Features Based Approach for Identification and Classification of Indian Medicinal Plants”, In: International Journal of Computer Applications (0975-8887), 2010, 6(12): 45-51.
[4]Basvaraj S. Anami, Vishwanath C. Burkpalli, “Color Based Identification and Classification of Boiled Food Grain Images”. In: International Journal of Food Engineering, 2009, 5(5):1-19.
[5]Bolle, R.M., Connell, J.H., Haas N., Mahon, R. and Taubin, G., “VeggieVision: A Produce Recognition System”. Technical Report forthcoming, IBM, 1996.
[6]B. S. Anami, D.G. Savakar, A. K. Kannur, M. V. Karali1, “Classification of Grapes Using Artificial Neural Network”, Proceedings of the International Conference on Cognition and Recognition, 2008, 436-442.
[7]Cheng-Jin Du, Da-Wen, “Pizza sauce spread classification using color vision and support vector machines”. Journal of Food Engineering, 2005, 66(2): 137-145.
[8]Fernandez C, Suardiaz J, Jimenez C, Navarao P. J, & Toledo A, Iborra A., “Automated Visual Inspection System for the Classification of Preserved Vegetables”, in Proc. of the IEEE International Symposium, 2002(1): 265-269.
[9]Gonzalez, R. C. and Woods, R. E., “Digital Image Processing”, Third Edition, Pearson Education Inc., 2008.
[10]Hetal N. Patel, Dr. R.K. Jain, Dr. M.V. Joshi, “Fruit Detection using Improved Multiple Features based Algorithm”, In: International Journal of Computer Applications (0975-8887), 2011, 13(2): 1-5.
[11]Kok-Meng Lee, Qiang Li, Wayne Daley, “Effects of Classification Methods on Color-Based Feature Detection With food Processing Applications”, In: IEEE Transactions on Automation Science and Engineering, 2007, 4(1): 40-51.
[12]Pedrerschi .F, D. Mery, F.Mendoza & J.M. Aguilera, “Classification of Potato Chips Using Pattern Recognition, Journal of Food Science”, 2004 ( 69): E264-270.
[13]S. Arivazhagan, R.Newlin Shebiah, S.Selva Nidhyanandhan, L.Ganesan, “Fruit Recognition using Color and Texture Features”, In: Journal of Emerging Trends in Computing and Information Sciences, 2010, 1(2): 90-94.
[14]S. Somatilake, A.N. Chalmers, “An Image-Based Food Classification System”, In: Proceedings of Image and Vision Computing, New Zealand, 2007: 260-265.
[15]Zhao-yan Liu, Fang Cheng, Yi-bin Ying, & Xiu-qin Rao, “Identification of rice seed varieties using neural network”. Journal of Zhejiang University SCIENCE B, 2005, 6(11):1095-1100.