CLASSIFICATION OF SELECTED APPLE FRUIT VARIETIES USING NAIVE BAYES
Journal Title: Indian Journal of Computer Science and Engineering - Year 2016, Vol 7, Issue 1
Abstract
Manual sorting of apple fruit varieties results to high cost, subjectivity, tediousness and inconsistency associated with human beings. A means for distinguishing apple varieties is needed and therefore, some reliable technique is needed to discriminate varieties rapidly and non-destructively. The main objective of this research was to investigate the applicability and performance of Naive Bayes algorithm in the classification of apple fruit varieties. The methodology involved image acquisition, pre-processing and segmentation, analysis and classification of apple varieties. Apple classification system prototype was built using MATLAB R2015a development platform environment. The results showed that the averaged values of the estimated accuracy, sensitivity, precision and specificity were 91%, 77%, 100% and 80% respectively. Through previous research works, the literature review identified MLP-Neural (Unay et al., 2006), fuzzy logic (Kavdir et al., 2003), principal components analysis (Bin et al., 2007) and neural networks (Ohali et al., 2011) as other technique which have been used previously to classify apple varieties. Comparison of their classification accuracy results with that of Naive Bayes technique showed that the accuracy of Naive Bayes was higher than the accuracy of principal components analysis, fuzzy logic and MLPNeural with 91%, 90%, 89%, and 83% respectively. This study indicated that Naive Bayes has good potential for identification of apple varieties nondestructively and accurately.
Authors and Affiliations
Misigo Ronald , Miriti Evans
Keywords
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