Defect Recognition of Fruit using Statistical Approach

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2015 by IJETT Journal
Volume-22 Number-7
Year of Publication : 2015
Authors : Chetana.K.Shetty, S.B.Kulkarni, U.P.Kulkarni, Ramesh.K, Ravindra.Hegadi
DOI :  10.14445/22315381/IJETT-V22P269


Chetana.K.Shetty, S.B.Kulkarni, U.P.Kulkarni, Ramesh.K, Ravindra.Hegadi"Defect Recognition of Fruit using Statistical Approach", International Journal of Engineering Trends and Technology (IJETT), V22(7),331-334 April 2015. ISSN:2231-5381. published by seventh sense research group

An automated fruit defect recognition and a review of previous defect detection methods are reported. To identify the defects in various digital images visual inspection systems are used. Visual inspection systems have a scanned copy of an object to find the flaws in the object. The visual inspection systems are used in fruit defect detection, textile fabric defect detection, metal crack detection etc. In this work defect detection algorithm focuses on the cropped image (excluding background) of standard size 200*200. The images should be captured with proper focus. The region of the image is equally divided and computing the mean value of each region. Calculating the minimum mean, maximum mean, difference (minimum mean, maximum mean) and average (minimum mean, maximum mean). If the difference is greater than average then it is defected. This methodology is able to recognize fruit defects in natural conditions.


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Min_mean, Max_mean, Diff (Min_mean, Max_mean), Avg (Min_mean, Max_mean)