Automated Skin Defect Identification System for Orange Fruit Grading Based on Genetic Algorithm

Using machine vision technology to grade oranges can ensure that only good-quality fruits are exported. One of the most prominent issues in the post-harvest processing of oranges is the efficient determination of skin defects with the intention of classifying the fruits depending on their external appearance. Shape, size, colour and texture are the important grading parameters that dictate the quality and value of many fruit products. The accuracy of the evaluation results is increased by proper combination of different grading parameters. This article presents an efficient orange surface grading system (normal and defective) based on the colour and texture features. As a part of the feature selection step, this article presents a wrapper approach with genetic algorithm to search out and identify the informative feature subset for classification. The selected features were subjected to various classifiers such as support vector machine, back propagation neural network and auto associative neural network (AANN) to study the performance analysis among these three classifiers. The results reveal that AANN classification algorithm has the highest accuracy rate of 94.5% among these three classifiers.

[1]  Vincent Leemans,et al.  A real-time grading method of apples based on features extracted from defects , 2004 .

[2]  Nuno M. Fonseca Ferreira,et al.  Introducing the fractional-order Darwinian PSO , 2012, Signal Image Video Process..

[3]  George C. Stockman,et al.  Tissue reflectance and machine vision for automated sweet cherry sorting , 1996, Other Conferences.

[4]  Weikang Gu,et al.  Computer vision based system for apple surface defect detection , 2002 .

[5]  Guojie Song,et al.  An Adaption of Relief for Redundant Feature Elimination , 2012, ISNN.

[6]  Fred A. Payne,et al.  COLOR AND DEFECT SORTING OF BELL PEPPERS USING MACHINE VISION , 1990 .

[7]  Tom C. Pearson,et al.  Machine vision system for automated detection of stained pistachio nuts , 1995, Other Conferences.

[8]  R. Wastie,et al.  Experiments in the detection of incipient diseases in potato tubers by optical methods , 1982 .

[9]  R. N. Shebiah,et al.  Fruit Recognition using Color and Texture Features , 2010 .

[10]  Bruce A. Draper,et al.  Feature selection from huge feature sets , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Meenu Dadwal,et al.  Estimate Ripeness Level of fruits Using RGB Color Space and Fuzzy Logic Technique , 2012 .

[12]  J. Blasco,et al.  Comparison of three algorithms in the classification of table olives by means of computer vision , 2004 .

[13]  C. T. Morrow,et al.  Grading of Mushrooms Using a Machine Vision System , 1994 .

[14]  M. J. Delwiche,et al.  Machine vision methods for defect sorting stonefruit , 1994 .

[15]  Cheng-Hong Yang,et al.  Reducing SAGE Data Using Genetic Algorithms , 2009 .

[16]  Yang Tao,et al.  Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines , 1999 .

[17]  M. J. Delwiche,et al.  PEACH DEFECT DETECTION WITH MACHINE VISION , 1991 .

[18]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[19]  K. Chen,et al.  Predicting beef tenderness using color and multispectral image texture features. , 2012, Meat science.

[20]  José Blasco,et al.  Citrus sorting by identification of the most common defects using multispectral computer vision , 2007 .

[21]  Y. Sarig,et al.  Defect sorting of dry dates by image analysis , 1993 .

[22]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[23]  Francisco Manzano-Agugliaro,et al.  High speed intelligent classifier of tomatoes by colour, size and weight , 2012 .

[24]  John M. Gauch,et al.  Comparison of three-color image segmentation algorithms in four color spaces , 1992, Other Conferences.