Fruit Quality Classification Using CNN

Abstract

Fruit Classification has become a riveting topic in computer vision. Traditional fruit classification processes are generally based on visual ability and such methods can be very tedious, inconsistent and time consuming. In agriculture science, fruit classification can be found highly beneficial. Hence researches in this area indicate the feasibility of using deep learning models to improve product quality while liberating people from the traditional hand sorting of fruits. In this project an extensive dataset of 3 varieties of fruits is considered. With this dataset, an ImageNet pre-trained convolutional neural network was fine-tuned to obtain a classifier. This classifier is optimized to obtain high accuracy in less time for the classification of rotten and fresh fruits.