Identification and Classification of Mango Fruits Using Image Processing

Authors

  • Dameshwari Sahu  Department of Electronics and Telecommunication Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India
  • Chitesh Dewangan  Department of Electronics and Telecommunication Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India

Keywords:

Image Processing, Mango Classification, Mango Identification, Fruit Grading, Defect Detection.

Abstract

Image processing technology has been widely used in the agricultural field. Most of it applied to the robot that can be used for picking fruit and for inspection vehicle. Identification and classification is a major challenge for the computer vision to achieve near human levels of recognition. The fruits and vegetable classification is useful in the supermarkets and can also be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of this work is to develop an automated tool, which can capable to identify and classify mango fruits based on shape, size and colour features by digital image analysis. Initially, pre-processing techniques will be adopted to obtain the binary image using the texture analysis and morphological operations on digital images of different mango fruits. Later, the processed images will be further classified by suitable classification method. MATLAB have been used as the programming tool for identification and classification of fruits using image processing toolbox. Proposed method can be used to detect the external defects, stems, size and shape of mangos, and to classify the mango in high speed and precision.

References

  1. C. S. Nandi, B. Tudu, and C. Koley, “A machine vision-based maturity prediction system for sorting of harvested mangoes,” IEEE Trans. Instrum. Meas., vol. 63, no. 7, pp. 1722–1730, 2014.
  2. C. Prieto and D. Carolina, “Classification of Oranges by Maturity, Using Image Processing Techniques,” in Proceedings of 3rd IEEE International Congress of Engineering Mechatronics and Automation (CIIMA), 2014, pp. 1–5.
  3. A. Rocha, D. C. Hauagge, J. Wainer, and S. Goldenstein, “Automatic fruit and vegetable classification from images,” Comput. Electron. Agric., vol. 70, no. 1, pp. 96–104, 2010.
  4. S. R. Dubey and A. S. Jalal, “Application of Image Processing in Fruit and Vegetable Analysis: A Review,” J. Intell. Syst., vol. 24, no. 4, pp. 405–424, 2015.
  5. A. M. Aibinu, M. J. E. Salami, A. A. Shafie, N. Hazali, and N. Termidzi, “Automatic Fruits Identification System Using Hybrid Technique,” in Proceedings of 6th IEEE International Symposium on Electronic Design, Test and Application, 2011, pp. 217–221.
  6. E. A. Murillo-Bracamontes, M. E. Martinez-Rosas, M. M. Miranda-Velasco, H. L. Martinez-Reyes, J. R. Martinez-Sandoval, and H. Cervantes-De-Avila, “Implementation of Hough Transform for fruit image segmentation,” in Procedia Engineering, 2012, vol. 35, pp. 230–239.
  7. V. Pham and B. Lee, “An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm,” Vietnam J. Comput. Sci., vol. 2, no. 1, pp. 25–33, 2015.
  8. A. Gongal, S. Amatya, M. Karkee, Q. Zhang, and K. Lewis, “Sensors and systems for fruit detection and localization: A review,” Comput. Electron. Agric., vol. 116, pp. 8–19, 2015.
  9. T. Meruliya, “Image Processing for Fruit Shape and Texture Feature Extraction - Review,” Int. J. Comput. Appl. (0975, vol. 129, no. 8, pp. 30–33, 2015.
  10. S. Poorani and P. G. Brindha, “Automatic detection of pomegranate fruits using K-mean clustering,” Int. J. Adv. Res. Sci. Eng., vol. 3, no. 8, pp. 198–202, 2014.
  11. “Image database: Mango ‘Kent,’” 2014. [Online]. Available:http://www.cofilab.com/portfolio/mangoesdb/. [Accessed: 01-Jul-2016].

Downloads

Published

2017-04-30

Issue

Section

Research Articles

How to Cite

[1]
Dameshwari Sahu, Chitesh Dewangan, " Identification and Classification of Mango Fruits Using Image Processing, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.203-210, March-April-2017.