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.

DOI: https://doi.org/10.1007/978-3-642-22606-9_8

Abstract

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.

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