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CITRUS FRUIT QUALITY CLASSIFICATION BASED ON SIZE USING DIGITAL IMAGE PROCESSING

https://doi.org/10.26898/0370-8799-2018-5-12

Abstract

A new computer vision algorithm for citrus fruit quality classification based on the size of a single tree fruits was developed in this study. The image properties of area, perimeter, and diameter for the citrus fruits were measured by pixels. In order to estimate citrus fruit size in a realistic manner, the ratios of diameter, perimeter and area in pixel values in relation to the actual size of one fruit were determined. The total of 1860 citrus fruits were grouped based on diameter, perimeter, and area in pixels. The results of the grouping of citrus fruits by diameter, perimeter and area were compared with the results of the survey research into citrus fruit size as conducted by the Jeju Citrus Commission. Comparative results reveal that the image of the citrus fruit diameter in pixels demonstrate a more accurate size than the other two pixel values, i.e. perimeter and area.

About the Authors

Ulzii-Orshikh Dorj
Chon Buk National University
Korea, Republic of

Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, 

664-14, IGa, DeokjinDong, Jeonju, Chon Buk, 561-756



Uranbaigal Dejidbal
Mongolian University of Life Sciences
Mongolia

Department of Mathematics, Physics and Information Technology, School of Engineering and Technology, 

Ulaanbaatar



Hongseok Chae
Chon Buk National University
Korea, Republic of

Center for Advanced Image and Information Technology, School of Electronics & Information Engineering,

Deokjin-Dong



Lkhagvadorj Batsambuu
Mongolian University of Life Sciences
Mongolia

Department of Food Processing and Hydraulic Engineering, School of Engineering and Technology, 

Ulaanbaatar



Altanchimeg Badarch
Mongolian University of Life Sciences
Mongolia

Department of Economic Statistics and Mathematical Modeling, School of Economics and Business, 

Ulaanbaatar



Shinebayar Dalkhaa
Mongolian University of Life Sciences
Mongolia

Department of Mathematics, Physics and Information Technology, School of Engineering and Technology, 

Ulaanbaatar



References

1. Anandkumar Patil, IshwarappaR К. Classification of crops using FCM segmentation and texture, color feature. World Jou rnal of Science and Technology, 2012, vol. 10, no. 2, pp. 74-78.

2. Suchitra A. Khoje, Bodhe S.K., AlpanaAdsul. Automated Skin Defect Identification System for Fruit Grading Based on Discrete Curvelet Transform. International Journal of Engineering and Technology, 2013, vol. 5, no. 4, pp. 3251-3256.

3. Jyoti A Kodagali, Balaji S. Computer Vision and Image Analysis Based Techniques for Automatic Characterization of Fruits -A Review. International Journal of Food Science & Technology (IJFST), 2012, vol. 2, no. 2, pp. 1-14.

4. KhalidM. Alrajeh, Tamer. A.A. Alzohairy. Date Fruits Classification using MLP and RBF Neural Networks. International Journal of Computer Applications (0975-8887), 2012, vol. 41, no. 10.

5. Yudong Zhang and Lenan Wu. Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine. Sensors, 2012, no. 12, pp. 12489-12505.

6. Mahendran R, Jayashree GC, Alagusundaram K. Application of Computer Vision Technique on Sorting and Grading ofFruits and Vegetables. Journal Food Processing & Technology, 2011, no. 5, pp. 1-7.

7. Devrim Unaya, Bernard Gosselinb, Olivier Kleynenc, Vincent Leemansc, Marie-France Destainc, Olivier Debeird. Automatic grading of Bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 2011, vol. 75, pp 204-212.

8. Elena Guzm6n, Vincent Baeten, Juan Antonio Fern6ndez Pierna, JosfiA. GarcHa-Mesa. Using a Visible Vision System for On-Line Determination of Quality Parameters of Olive Fruits. Food and Nutrition Sciences, 2013, vol. 4, pp. 90-98.

9. Amir Alipasandi, Hosein Ghaffari, Saman Zohrabi Alibeyglu. Classification of three Varieties of Peach Fruit Using Artificial Neural Network Assisted with Image Processing Tecbniques.InternationalJournalofAgronomy and Plant Production, 2013, vol., 9, no. 4, pp.2179-2186.

10. Deepa P., Geethalakshmi S.N. A Comparative Analysis of Feature Extraction Methods for Fruit Grading Classifications. International Journal of Emerging Technologies in Computational and Applied Sciences, 2013, pp.221-225.

11. Sajad Sabzi, Payam Javadikia, Hekmatrabbani, Ali Adelkhani. Promote of Sorting System of Bam orange using an adaptive neural-fuzzy inference system (ANFIS). International Journal of Agriculture and Crop Sciences, 2013, vol., 14, no. 5, pp. 1529-1534.


Review

For citations:


Dorj U., Dejidbal U., Chae H., Batsambuu L., Badarch A., Dalkhaa Sh. CITRUS FRUIT QUALITY CLASSIFICATION BASED ON SIZE USING DIGITAL IMAGE PROCESSING. Siberian Herald of Agricultural Science. 2018;48(5):95-101. (In Russ.) https://doi.org/10.26898/0370-8799-2018-5-12

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ISSN 0370-8799 (Print)
ISSN 2658-462X (Online)