Abstract:
Bananas are one of the most widely consumed fruits worldwide, and they come in various
shapes, sizes, and colors. Bananas are one of the few tropical crops that have not been bred
successfully, and all presently cultivated varieties are natural selections. Traditionally, human
experts perform the classification based on visual inspection, which can be subjective and
time-consuming. Therefore, developing an automated system that can accurately classify
bananas based on visual features would be highly beneficial. Feature engineering became
easier after the Convolutional Neural Network (CNN) was developed. Five distinct varieties
of bananas were categorized in this proposed work using the CNN model. To improve
classification performance, many training images are needed when using the CNN model for
classification. Both the original and enhanced images were used to train and evaluate the
suggested CNN model. During training, the CNN model achieved an overall validation
accuracy of 87%.
