Mehran University Research Journal Of Engineering &
Technology (HEC Recognized In Category "X")
Publishing Since 1982.



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Classification of Broken Rice Kernels using 12D Features

Keywords: Rice Classification, Feature Extraction, Image Analysis, Hough Transform, Radial Basis Function.

Mehran University Research Journal of Engineering & Technology

Volume 35 ,  Issue 3

SUNDER  ALI  KHOWAJA,FARZANA  RAUF  ABRO, SHEERAZ MEMON   ,PARUS KHUWAJA   

Abstract

Integrating the technological aspect for assessment of rice quality is very much needed for the Asian markets where rice is one of the major exports. Methods based on image analysis has been proposed for automated quality assessment by taking into account some of the textural features. These features are good at classifying when rice grains are scanned in controlled environment but it is not suitable for practical implementation. Rice grains are placed randomly on the scanner which neither maintains the uniformity in intensity regions nor the placement strategy is kept ideal thus resulting in false classification of grains. The aim of this research is to propose a method for extracting set of features which can overcome the said issues. This paper uses morphological features along-with gray level and Hough transform based features to overcome the false classification in the existing methods. RBF (Radial Basis function) is used as a classification mechanism to classify between complete grains and broken grains. Furthermore the broken grains are classified into two classes? i.e. acceptable grains and non-acceptable grains. This research also uses image enhancement technique prior to the feature extraction and classification process based on top-hat transformation. The proposed method has been simulated in MATLAB to visually analyze and validate the results.