DOI : https://doi.org/10.18517/ijaseit.10.5.8047

Partial Centroid Contour Distance (PCCD) in Mango Leaf Classification

Eko Prasetyo (1), Raden Dimas Adityo (2), Nanik Suciati (3), Chastine Fatichah (4)
(1) Department of Informatics, Engineering Faculty, Universitas Bhayangkara Surabaya, Surabaya, 60231, Indonesia
(2) Department of Informatics, Engineering Faculty, Universitas Bhayangkara Surabaya, Surabaya, 60231, Indonesia
(3) Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
(4) Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
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How to cite (IJASEIT) :
Prasetyo, Eko, et al. “Partial Centroid Contour Distance (PCCD) in Mango Leaf Classification”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, Oct. 2020, pp. 1920-6, doi:10.18517/ijaseit.10.5.8047.
Citation Format :
The research in the classification of mango leaf varieties requires appropriate features and classification methods to achieve high accuracy. The system used 263 features, texture and color features included Boundary Moments features that generated from Centroid Contour Distances (CCD). The CCD measures distance from center to the edge along 360 degrees, this causes enormous computational loads. On the other hand, the final part of mango leaf to recognize the mango varieties simply by observing the leaf base and leaf tip, so the mango leaf as the special case of CCD can be solved by only generating features at these parts. We propose Partial CCD (PCCD) by calculating the distance from boundary point does not to the center point of the leaf but to the midpoint-cut of the leaf base or leaf tip. PCCD has two parts, PCCD Leaf Base and PCCD Leaf Tip to capture leaf base and leaf tip features, respectively. On experiment testing with PCCD or another color, shape, and texture features only, the system can’t achieve high accuracy, but the combination of all features increase accuracy up to 10%. The comparison among all various features are used in classification. It is compared the original features, individual PCCD features (Leaf base and Leaf tip), and combination of Leaf base and Leaf tip. These results show that combination of original features and PCCD features achieve the best accuracy 80.17% and average accuracy 78.41%. The highest accuracy performance obtained by SVM classification is 81.73%. The comparison with other features also proved that the combination obtains better performance.

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