the real time operation in the validation phase further
confirm the results obtained in the training and
classifying phases. In fact, efficiency achieved using
proposed virtual grader is 99%, if manual grading is
assumed to be 100% efficient as reference level.
However this1% variation was due to subjective
judgment of human graders in perceiving the apple fruit
during manual grading, which of course, is inevitable.
Moreover, the repeatability of the proposed system was
found to be 100% as confirmed after rigorous
experimental validation. Achievement of 99% accuracy
at repeatability of 100%, established that Euclidean
distance metric based K-NN classifier was an efficient
method to translate human visual perception of grading
the apple based on fruit size into machine vision.
However, the manual grading was always manifested
with subjective tolerance. This fact was also confirmed
by three human experts chosen for manual grading.
According to them, it was not possible for them also to
decide the border cases.