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

Evy Kamilah Ratnasari


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%.

Full Text:



Rocha, Anderson, et al. 2010. “Automatic fruit and vegetable

classification from images”. Computers and Elec-tronics in

Agriculture 70 (2010) 96–104

S.Arivazhagan, et al. 2010. “Fruit Recognition using Color and

Texture Features”. CIS journal: Journal of Emerging Trends in

Computing and InformationSciences

Zhang, Yudong dan Lenan Wu. 2012. “Classification of Fruits

Using Computer Vision and a Multiclass Support Vector

Machine”. sensors 12.9 (2012): 12489-12505.

Faria, Fabio Augusto, et al. "Automatic Classifier Fusion for

Produce Recognition." Graphics, Patterns and Im-ages

(SIBGRAPI), 2012 25th SIBGRAPI Conference on. IEEE, 2012.

Seng, W. C., & Mirisaee, S. H. 2009. “A new method for fruits

recognition system”. Electrical Engineering and Informatics,

ICEEI'09. International Conference on (Vol. 1, pp. 130-


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.

Tam, W. K., & Lee, H. J. 2012. “Dental shade matching using a

digital camera”. Journal of Dentistry, 40, e3-e10.

Busin, L., Vandenbroucke, N., & Macaire, L. (2008). “Color

spaces and image segmentation”. Advances in im-aging and

electron physics, 151, 65-168.

Cardani, D. (2001). “Adventures in HSV space”. Vision and

Image Sciences Laboratory Department of Electri-cal

Engineering Technion - Institute of Technology 32000 Haifa


Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (Agustus

. Leaf classification using shape, color, and texture

features. International Journal of Computer Trends and

Technology, 225-230.

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




  • There are currently no refbacks.

Copyright (c) 2016 Evy Kamilah Ratnasari

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

INFORM: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
ISSN 2502-3470 (Print) | 2581-0367 (Online)
Published by Pusat Pengelola Jurnal, Universitas Dr. Soetomo
Managed by Program Studi Teknik Informatika, Fakultas Teknik, Universitas Dr. Soetomo
Address Jl. Semolowaru no 84, Surabaya, 60118, (031) 5944744
email [email protected]

Inform is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View Inform Stats

Inform is supervised by Relawan Jurnal Indonesia.