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Analysis And Detection of Infected Fruit Part Using Improved k-means Clustering and Segmentation Techniques

Journal: International Journal for Scientific Research and Development | IJSRD (Vol.3, No. 10)

Publication Date:

Authors : ; ; ; ; ;

Page : 467-470

Keywords : Improved K-means Algorithm; Clustering; Image Processing; Defect Segmentation;

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Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.

Last modified: 2016-01-07 18:55:53