Pengenalan Jenis Buah pada Citra Menggunakan Pendekatan Klasifikasi Berdasarkan Fitur Warna Lab dan Tekstur Co- Occurrence

  • Evy Kamilah Ratnasari
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Abstract

Abstract — Fruit recognition can be automatically applied to the field of education, industry, sales, as well as science. In the vision of computer recognition of fruit relies on four basic features that describe the characteristics of the fruit, i.e., size, color, shape, and texture. The fruit recognition through the RGB image results of cameras using the features of shape and size are not reliable and effective, because in a real data image can be composed of several different sizes of fruit on each type of fruit so it can't be identified morphologically the fruit size and uniformity that can affect the results of the classification. This journal based on the feature recognition method of building colors and textures for the classification of fruit.The classification is done by K-Nearest Neighbor based on color and texture features co-occurrence. Experimental results of 1882 dataset image of fruit for 12 different classes can recognize the fruit in both color and texture features based with the highest accuracy of 92%.

References

[1] Rocha, Anderson, et al. 2010. “Automatic fruit and vegetable
classification from images”. Computers and Elec-tronics in
Agriculture 70 (2010) 96–104
[2] S.Arivazhagan, et al. 2010. “Fruit Recognition using Color and
Texture Features”. CIS journal: Journal of Emerging Trends in
Computing and InformationSciences
[3] Zhang, Yudong dan Lenan Wu. 2012. “Classification of Fruits
Using Computer Vision and a Multiclass Support Vector
Machine”. sensors 12.9 (2012): 12489-12505.
[4] Faria, Fabio Augusto, et al. "Automatic Classifier Fusion for
Produce Recognition." Graphics, Patterns and Im-ages
(SIBGRAPI), 2012 25th SIBGRAPI Conference on. IEEE, 2012.
[5] Seng, W. C., & Mirisaee, S. H. 2009. “A new method for fruits
recognition system”. Electrical Engineering and Informatics,
2009. ICEEI'09. International Conference on (Vol. 1, pp. 130-
134). IEEE.
[6] Mendoza, F., Dejmek, P., & Aguilera, J. M. 2006. “Calibrated
color measurements of agricultural foods using image analysis”.
Postharvest Biology and Technology, 41(3), 285-295.
[7] Tam, W. K., & Lee, H. J. 2012. “Dental shade matching using a
digital camera”. Journal of Dentistry, 40, e3-e10.
[8] Busin, L., Vandenbroucke, N., & Macaire, L. (2008). “Color
spaces and image segmentation”. Advances in im-aging and
electron physics, 151, 65-168.
[9] Cardani, D. (2001). “Adventures in HSV space”. Vision and
Image Sciences Laboratory Department of Electri-cal
Engineering Technion - Institute of Technology 32000 Haifa
Israel.
[10] Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (Agustus
2011). Leaf classification using shape, color, and texture
features. International Journal of Computer Trends and
Technology, 225-230.
[11] Reddy, R. Obula Konda, dkk. (2013). “Classifying Similarity
and Defect Fabric Textures based on GLCM and Binary Pattern
Schemes”. I.J. Information Engineering and Electronic Business
5:25-33
Published
2016-07-31
How to Cite
RatnasariE. K. (2016). Pengenalan Jenis Buah pada Citra Menggunakan Pendekatan Klasifikasi Berdasarkan Fitur Warna Lab dan Tekstur Co- Occurrence. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 1(2). https://doi.org/10.25139/inform.v1i2.846
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Articles