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

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 9
Year of Publication: 2013
Authors:
Suchitra Khoje
Shrikant Bodhe
10.5120/9953-4320

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

@article{key:article,
	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}
}

Abstract

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.

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