Classification of Broken Rice Kernels using 12D Features

Sunder Ali Khowaja,Sheeraz Memon,Parus Khuwaja,Farzana Rauf Abro

doi:10.22581/muet1982.1603.10

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.

Highlights

Agricultural Industry is referred to as one of the widespread and largest industry of the world
Se and Sp are the ratios of complete rice kernels and broken rice kernels in tier 1 and the ratio of acceptable broken rice kernels and unacceptable broken rice kernels
This paper proposes a method based on 12D features which include morphological features, gray-level based features and Hough transform based feature to classify the rice kernels using RBF

Summary

Introduction

Agricultural Industry is referred to as one of the widespread and largest industry of the world. Recent advancement in technology has encouraged the researchers to work on developing algorithms for automated way of quality assessment replacing human inspection by machine intelligence [1]. Amongst all agricultural products rice is considered to be one of the major commodities as it is the major source of food for half of the world population. Apart from this rice is considered to be the most flexible cereal as it adapts the agro-ecological variations suggesting that it can grow under saline conditions, in flooded areas, in freshwater and in dry fields as well [2]. Various studies have suggested that by employing machine vision algorithms high quality standards can be achieved accurately along with a cost effective approach [3]

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