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
Fulltext View | Download
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.
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.

S. Thawkar and Ingolikar, R., “Automatic Detection and Classification of Masses in Digital Mammograms,” Int. J. Intell. Eng. Syst., vol. 10, no. 1, pp. 65-74, 2017.

K. G. Satheesh and A. N. J. Raj, “Medical image segmentation and classification using MKFCM and hybrid classifiers,” Int. J. Intell. Eng. Syst., 2017, doi: 10.22266/ijies2017.1231.02.

G. J. Jong, Hendrick, Z. H. Wang, D. Kurniadi, Aripriharta, and G. J. Horng, “Implementation of Otsu’s method in vein locator devices,” Int. J. Adv. Sci. Eng. Inf. Technol., 2018, doi: 10.18517/ijaseit.8.3.4414.

A. P. Rahmadini, P. Kristalina, and A. Sudarsono, “Optimization of fingerprint indoor localization system for multiple object tracking based on iterated weighting constant - KNN method,” Int. J. Adv. Sci. Eng. Inf. Technol., 2018, doi: 10.18517/ijaseit.8.3.6086.

K. S. Kumar and G. Sreenivasulu, “Image enhancement through denoising and retrieval of vegetation parameters from Landsat8,” Int. J. Adv. Sci. Eng. Inf. Technol., 2018, doi: 10.18517/ijaseit.8.1.4059.

Y. Shi et al., “Tree species classification using plant functional traits from LiDAR and hyperspectral data,” Int. J. Appl. Earth Obs. Geoinf., vol. 73, pp. 207-219, Dec. 2018, doi: 10.1016/j.jag.2018.06.018.

Y. Zhang, W. S. Lee, M. Li, L. Zheng, and M. A. Ritenour, “Non-destructive recognition and classification of citrus fruit blemishes based on ant colony optimized spectral information,” Postharvest Biol. Technol., vol. 143, pp. 119-128, Sep. 2018, doi: 10.1016/j.postharvbio.2018.05.004.

M. A. Peña, R. Liao, and A. Brenning, “Using spectrotemporal indices to improve the fruit-tree crop classification accuracy,” ISPRS J. Photogramm. Remote Sens., vol. 128, pp. 158-169, Jun. 2017, doi: 10.1016/j.isprsjprs.2017.03.019.

O. M. Ben Saeed et al., “Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system,” Comput. Electron. Agric., vol. 82, pp. 55-60, Mar. 2012, doi: 10.1016/j.compag.2011.12.010.

Y. Zhang, S. Wang, G. Ji, and P. Phillips, “Fruit classification using computer vision and feedforward neural network,” J. Food Eng., vol. 143, pp. 167-177, Dec. 2014, doi: 10.1016/j.jfoodeng.2014.07.001.

J. Steinbrener, K. Posch, and R. Leitner, “Hyperspectral fruit and vegetable classification using convolutional neural networks,” Comput. Electron. Agric., vol. 162, pp. 364-372, Jul. 2019, doi: 10.1016/j.compag.2019.04.019.

A. Sinha and R. S. Shekhawat, “Olive Spot Disease Detection and Classification using Analysis of Leaf Image Textures,” in Procedia Computer Science, Jan. 2020, vol. 167, pp. 2328-2336, doi: 10.1016/j.procs.2020.03.285.

M. Ben Haj Rhouma, J. Žunić, and M. C. Younis, “Moment invariants for multi-component shapes with applications to leaf classification,” Comput. Electron. Agric., vol. 142, pp. 326-337, Nov. 2017, doi: 10.1016/j.compag.2017.08.029.

S. Zhang, W. Huang, Y. Huang, and C. Zhang, “Plant Species Recognition Methods using Leaf Image: Overview,” Neurocomputing, May 2020, doi: 10.1016/j.neucom.2019.09.113.

G. Saleem, M. Akhtar, N. Ahmed, and W. S. Qureshi, “Automated analysis of visual leaf shape features for plant classification,” Comput. Electron. Agric., vol. 157, pp. 270-280, Feb. 2019, doi: 10.1016/j.compag.2018.12.038.

C. Kalyoncu and í–. Toygar, “Geometric leaf classification,” Comput. Vis. Image Underst., vol. 133, pp. 102-109, Apr. 2015, doi: 10.1016/j.cviu.2014.11.001.

A. Hasim, Y. Herdiyeni, and S. Douady, “Leaf Shape Recognition using Centroid Contour Distance,” in IOP Conference Series: Earth and Environmental Science, Feb. 2016, vol. 31, no. 1, doi: 10.1088/1755-1315/31/1/012002.

M. S. Mohd Asaari, S. A. Suandi, and B. A. Rosdi, “Fusion of Band Limited Phase only Correlation and Width Centroid Contour Distance for finger based biometrics,” Expert Syst. Appl., 2014, doi: 10.1016/j.eswa.2013.11.033.

B. Wang, D. Brown, Y. Gao, and J. La Salle, “MARCH: Multiscale-arch-height description for mobile retrieval of leaf images,” Inf. Sci. (Ny)., 2015, doi: 10.1016/j.ins.2014.07.028.

J. R. Kala and S. Viriri, “Plant species classification using sinuosity coefficients of leaves,” Image Anal. Stereol., 2018, doi: 10.5566/ias.1821.

E. Farnell et al., “A shape-context model for matching placental chorionic surface vascular networks,” Image Anal. Stereol., 2018, doi: 10.5566/ias.1708.

E. Prasetyo, R. D. Adityo, N. Suciati, and C. Fatichah, “Multi-class K-support vector nearest neighbor for mango leaf classification,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 4, 2018, doi: 10.12928/TELKOMNIKA.v16i4.8482.

E. Prasetyo, R. D. Adityo, N. Suciati, and C. Fatichah, “Average and maximum weights in weighted rotation- and scale-invariant LBP for classification of mango leaves,” J. Theor. Appl. Inf. Technol., vol. 95, no. 21, 2017.

E. Prasetyo, R. D. Adityo, N. Suciati, and C. Fatichah, “Mango Leaf Classification with Boundary Moments of Centroid Contour Distances as Shape Features,” 2018, doi: 10.1109/ISITIA.2018.8711115.

D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognit., 2004, doi: 10.1016/j.patcog.2003.07.008.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). 2007.

Y. Mingqiang, K. Kidiyo, and R. Joseph, “A Survey of Shape Feature Extraction Techniques,” in Pattern Recognition Techniques, Technology and Applications, 2008.

E. Prasetyo, R. D. Adityo, N. Suciati, and C. Fatichah, “Mango leaf image segmentation on HSV and YCbCr color spaces using Otsu thresholding,” 2017, doi: 10.1109/ICSTC.2017.8011860.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).