Classification of Broken Rice Kernels using 12D Features

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

Author(s)

SUNDER ALI KHOWAJA Institute of Information & Communication Technology, University of Sindh, Jamshoro, Pakistan

FARZANA RAUF ABRO Department of Electronic Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan

SHEERAZ MEMON Department of Computer Systems Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan

PARUS KHUWAJA Institute of Business Administration, University of Sindh,Jamshoro, Pakistan

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.

Publication Details

Page(s) 401-412

DOI DOI not available

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 35, Issue: 3, Year: 2016

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Keywords

Rice Classification