An Image Processing Algorithm for Varietal Classification of Sapota Fruits

Authors

  • B. L. Benisha Bennet Department of ECE, Velammal College of Engineering and Technology (Affiliated to Anna Univ., Chennai), Madurai, India

DOI:

https://doi.org/10.37591/joosdt.v1i2.158

Abstract

In several cases, fruits of different varieties are often mixed into the same bulk and reach the market/processing unit. In such scenarios, variations in product properties due to varietal variations result in inconsistent product end-product quality. This is of important concern in case of sapota; particularly between Cricket ball and P[ala varieties, the most dominant varieties grown in India. Hence, in the present study, a machine vision system consisting of a camera, illumination source, camera holding unit, sample platform, and computer was used to quickly differentiate between Cricket ball and Pala varieties of sapota fruit. The sphericity of the sample was used as the classification parameter. The developed image processing methodology gave an overall classification efficiency of around 95%.

Keywords: Machine vision, Image processing, Sphericity, Classification efficiency

References

Pujari S. Available at: http://www.yourarticlelibrary.com, Sapota Cultivation in India – Production Area, Climate, Harvesting and Fruit Handling. As accessed on 02.08.14. 2. Mickelbart M V. Sapodilla: A Potential Crop for Subtropical Climates. Progress in New Crops. ASHS Press, Alexandria, 1996. VA, 439–446p.

Benisha B. A Machine Vison Approach for Grading of Onions. J. Image Process. Patt. Recog. Progress. 2014; 1(2): 11–14p. 4. Sahay K M, Singh K K. Unit Operations of Agricultural Processing. Vikas Publishing House Pvt. Ltd. 2004.

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Published

2016-01-08

Issue

Vol. 1 No. 2 (2014)

Section

Research Articles

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