Automated grading of mangoes based on surface defect detection using a combined approach of image segmentation


Neeraj Kumari
Ashutosh Kumar Bhatt
Rakesh Kumar Dwivedi
Rajendra Belwal


Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task. In image segmentation algorithms, a single segmentation technique is not sufficient in providing accurate segmentation results in many cases. In this paper we are proposing a combining approach of image segmentation techniques for improving segmentation accuracy. As a case study fruit mango is selected for classification based on surface defect. This classification method consists of three steps: (a) image pre-processing, (b) feature extraction and feature selection and (c) classification of mango. Feature extraction phase is performed on an enhanced input image. In feature selection PCA methodology is used. In classification three classifiers BPNN, Naïve bayes and LDA are used. Proposed image segmentation technique is tested on online dataset and our own collected images database. Proposed segmentation technique performance is compared with existing segmentation techniques. Classification results of BPNN in training and testing phase are acceptable for proposed segmentation technique.


How to Cite
Kumari, N., Kumar Bhatt , A. ., Kumar Dwivedi , R. ., & Belwal , R. . (2020). Automated grading of mangoes based on surface defect detection using a combined approach of image segmentation. Environment Conservation Journal, 21(3), 17-23.


  1. Arivazhagan S., Shebiah R. Newlin., Ananthi S., Vishnu Varthini S. 2013. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int.: CIGR J. 15 (1): 211–217.
  2. Blasco, J., Aleixos, N. and Molto, E. 2007. Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering , Elsevier. 81(3): 535-543.
  3. Gonzalez, R.C. and Woods, R.E. 2002. Digital image processing. NJ, USA: Prentice Hall.
  4. Kumari, N., Bhatt, A.K., Dwivedi, R.K. and Belwal, R. 2019. Performance analysis of support vecor machine in defective and non defective mangoes classification," ,IJEAT. Volume 8(4), 1563-1572.
  5. Leemans, V., Magein, H. and Destain, M.F. 1998. Defect segmentation on ‘golden delicious’ apples by using colour machine vision. Comput Electron Agri., 20(2): 117–130
  6. Lucchese, L., and Mitra, S.K. 1999. Unsupervised low-frequency driven segmentation of color images. In Proceedings of the 1999 IEEE International conference on image processing (pp. 240–244). Kobe, Japan.
  7. Momin, M.A, Rahman, M.T., Sultana, M.S., Igathinathana, C. and Ziauddin, A.T.M. 2017. Geometry based mass grading of mango fruits using image processing”, Information processing in agriculture. Volume 4(2),150-160.
  8. Nandi, C.S, Tudu, B. and Koley, C. 2014. Computer vision based mango fruit grading system, ICIET. 1-5.
  9. Pham, V.H. and Lee B.R. 2015. An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Vietnam Journal of computer science. Volume 2, 25-33.
  10. Qin, J., Burks, T. F., Kim, D. G., & Bulanon, D. M. 2008. Classification of citrus peel diseases using color texture feature analysis. In Food Processing Automation Conference Proceedings, 28-29 June 2008, Providence, Rhode Island (p. 31). American Society of Agricultural and Biological Engineers.

Similar Articles

You may also start an advanced similarity search for this article.