Abstract
Jujube is one of the popular fruits that possess high nutritional
components and have economic value. Grading of jujube is a post-harvest
process applied in the fruit industry for the tasks like fruit quality
check, fruit species identification, price labelling, edibility duration
estimation, safety, etc. This research investigates into the suggested
7-layer CNN model and two classical models (i.e., VGG16 and AlexNet) for
grading jujube fruits according to their stages of maturity. Primarily,
jujube of four different maturity grades was identified on the field and
collected from the field manually and their images were captured through
a machine vision system. Further, image pre-processing and augmentation
were performed to get the training/testing-ready dataset. Finally, a
7-layer CNN model was deployed and grading performance was examined over
the original and augmented dataset using performance metrics of
precision, sensitivity, and F1-measure. Furthermore, the model’s
classification accuracy was compared to that of classical models, where
the proposed model surmounts both the classical models. Results reveal
that the proposed model attained a high grading accuracy of 99.44% and
97.53% over the augmented and original dataset respectively. Also, the
computation time and training parameters count were reduced to almost
one-tenth and one-third of that of VGG16 and AlexNet models. Results
advocate that the classical model could be replaced with the proposed
models and can be further investigated for other fruits for better
classification accuracy, reduced parameters and reduced computational
time.