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Comparative Performance Evaluation of Size Metrics and Classifiers in Computer Vision based Automatic Mango Grading

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 9
Year of Publication: 2013
Suchitra Khoje
Shrikant Bodhe

Suchitra Khoje and Shrikant Bodhe. Article: Comparative Performance Evaluation of Size Metrics and Classifiers in Computer Vision based Automatic Mango Grading. International Journal of Computer Applications 61(9):1-7, January 2013. Full text available. BibTeX

	author = {Suchitra Khoje and Shrikant Bodhe},
	title = {Article: Comparative Performance Evaluation of Size Metrics and Classifiers in Computer Vision based Automatic Mango Grading},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {61},
	number = {9},
	pages = {1-7},
	month = {January},
	note = {Full text available}


Any horticulture product is expected to meet the set quality standards in order to improve its market value. The mango is one of the most delicious tropical fruit from India. Exports from India account for 0. 11 per cent of the total domestic production as against about 4 per cent in case of other exporting countries like Mexico, Philippines and Venezuela. Case study of mango fruit from Maharashtra, India is being considered here for size grading purpose. Images of mango are captured using CCD camera and size analysis is carried out using the MATLAB package. The paper discusses various size estimation metrics which are used as feature vectors for two classifiers namely Feed Forward Neural network (FFNN) and Support Vector Machines. The performance of these classifiers in grading mangoes according to CODEX size standards is presented. Experimental results show that Statistical method give an average size grading efficiency of 97% irrespective of classifiers for mango size grading.


  • Blasco and et. al. Automatic sorting of satsuma (citrus unshiu) segments using computer vision and morphological features. Computer and Electronics in agriculture, 66:1–8, 2009.
  • Tadhg Brosnan and Dawen Sen. Inspecting and grading of agricultural and food products by computer vision system-a review. Computer and Electronics in Agriculture, 36:193– 213, 2002.
  • Tadhg Brosnan and Da-Wen Sun. Improving quality inspection of food products by computer visiona review. Journal of Food Engineering, 61:3–16, 2004.
  • Moreda GP, Caavate JO, Garc-Ramos FJ, and Ruiz-Altisent M. Non-destructive technologies for fruit and vegetable size determination a review. Journal of Food Engineering, 92:119–136, 2009.
  • Bundit Jarimopas and Nitipong Jaisin b. An experimental machine vision system for sorting sweet tamarind. Journal of Food Engineering, 89:291–297, 2008.
  • Lawless and Heymann. Physiological and Psychological Foundations of Sensory Function, Chapter 2, Sensory Evaluation of Food principles and practices. springer, second edition, 2010.
  • Paolo Menesattia, Corrado Costab, Graziella Pagliaa, Federico Pallottinoa, Stefano D'Andreab, Valentina Rimatoria, and Jacopo Aguzzicand. Shape-based methodology for multivariate discrimination among italian hazelnut cultivars. Biosystem Engineering, 101:417–424, 2008.
  • Paulus and Schrevens. Shape characterization of new apple cultivars by fourier expansion of digitized images. Journal of Agricultural Engineering Research, 72:113–118, 1999.
  • P. Sudhakara Rao and S. Renganathan. New approaches for size determination of apple fruits for automatic sorting and grading. Iranian Journal of Electrical and Computer Engineering, 1:90–97, 2002.
  • Ruiz-Altisent, Ruiz-Garcia, Moreda, Hernandez-Sanchez, Correa, Diezma, and Garcia-Ramos. Sensors for product characterization and quality of specialty crops - a review. Computers and Electronics in Agriculture, 74:176–194, 2010.