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Morphology based feature extraction and recognition for enhanced wheat quality evaluation

Chhabra, Manish and Reel, Parminder (2011). Morphology based feature extraction and recognition for enhanced wheat quality evaluation. In: Aluru, Srinivas; Bandyopadhyay, Sanghamitra; Catalyurek, Umit V.; P. Dubhashi, Devdatt; Jones, Phillip H.; Parashar, Manish and Schmidt, Bertil eds. Contemporary Computing. Communications in Computer and Information Science (168). Heidelberg: Springer, pp. 41–50.

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Wheat grain quality assessment is important in meeting market requirements. The quality of the wheat can be judge byits length, thickness, width, area, etc. In this paper on the basis of simple mathematical calculations different parameters of a number of wheat grains are calculated. The present paper focused on the classification of wheat grains using morphological. The grain types used in this study were Hard Wheat, Tender Wheat. In this paper the application of neural network is used for assessment of wheat grain. The contours of whole and broken grains have been extracted, precisely normalised and then used as input data for the neural network. The network optimisation has been carried out and then the results have been analysed in the context of response values worked –out by the output neurons.

Item Type: Book Section
Copyright Holders: 2011 Springer-Verlag
ISBN: 3-642-22605-1, 978-3-642-22605-2
ISSN: 1865-0929
Extra Information: 4th International Conference, IC3 2011, Proceedings
Noida, India, August 8-10, 2011
ISSN 1865-0929, e-ISSN 1865-0937
DOI 10.1007/978-3-642-22606-9
Keywords: wheat quality assessment; image recognition; feature extraction; image segmentation; neural network; MATLAB GUI
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 34051
Depositing User: Parminder Reel
Date Deposited: 23 Jul 2012 08:59
Last Modified: 12 Jun 2020 16:58
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