Classification of Mangosteen Surface Quality Using Principal Component Analysis

Slamet Riyadi, Amelia Mutiara Ayu Ratiwi, Cahya Damarjati, Tony K. Hariadi, Indira Prabasari, Nafi Ananda Utama

Abstract


Mangosteen (Garcinia mangostana L) is one of the primary contributor for Indonesia export. For export commodity, the fruit should comply the quality requirement including its surface. Presently, the surface is evaluated by human visual to classify between defect and non- defect surface. This conventional method is less accurate and takes time, especially in high volume harvest. In order to overcome this problem, this research proposed images processing based classification method using principal component analysis (PCA). The method involved pre-processing task, PCA decomposition, and statistical features extraction and classification task using linear discriminant analysis. The method has been tested on 120 images by applying 4-fold cross validation method and achieve classification accuracy of 96.67%, 90.00%, 90.00% and 100.00% for fold-1, fold-2, fold-3 and fold-4, respectively. In conclusion, the proposed method succeeded to classify between defect and non-defect mangosteen surface with 94.16% accuracy.

Keywords


Mangosteen, Principal Component Analysis, statistical features extraction, Linear Discriminant Analysis, K- Fold Cross Validation

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References


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DOI: https://doi.org/10.18196/eist.115

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