A Comparative Analysis of Feature Extraction Methods for Fruit Grading Classillcations

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dc.categoryJournal Article
dc.contributor.authorGeethalakshmi, S N
dc.date.accessioned2017-03-28T23:28:12Z
dc.date.available2017-03-28T23:28:12Z
dc.date.issued2013
dc.departmentComputer Scienceen_US
dc.description.abstractFruit plays an important role in our life. India is exporting large number o f fruits to abroad in that mosambi is one among. Before exporting the fruits, the fruits have to be graded according to their quality. This paper presents an evaluation and comparison o f the performance o f three different extraction methods for classification of defect and non defect fruits. Three different feature extraction methods are GLCM (Grey Level Co-occurrence Matrix), shape features and intensity based features. The performance of each feature extraction method is evaluated and compared based on PNN (Probabilistic Neural Network) classifier. The experimental results suggest that shape feature outperformed than other two methods.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2256
dc.langEnglishen_US
dc.publisher.nameInternational Journal of Emerging Technologies in Computational and Applied Sciencesen_US
dc.publisher.typeInternationalen_US
dc.titleA Comparative Analysis of Feature Extraction Methods for Fruit Grading Classillcationsen_US
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