human graders, machine vision is the most effective and
non-destructive evaluation technique. Assessment of
apple quality based on its size is highly subjective due
to a number of factors that may influence the crop
maturity during cultivation such as geographical
location, weather conditions, rainfall density, nurturing
ingredients, disease and industrial effluence, etc. Due to
highly subjective nature of the apple quality, it is indeed
extremely difficult to make any benchmark or standards
for size-based quality assessment. Most of the fruit
packaging industries, in fact, largely depend upon the
decision of the human experts in assessing or assigning
the grade to a particular size of the apple. However,
manual grading is obviously a very cumbersome
process as far as efficiency and accuracy are
concerned. In order to circumvent these difficulties,
machine vision based intelligent systems are required
urgently to replace human graders for assessing fruit
quality. An attempt is made in the present work to
replace human grader with a virtual grader for assessing
the apple quality based on its size using machine vision.
The knowledge or intelligence acquired by the human
grader with experience in grading apple based on its
size is, in fact, imbibed artificially in the proposed virtual
grader. Different algorithms had been developed for size
determination of fruits, cereals, vegetables and food
products under the realm of image processing in the
past, which are detailed below