Paper Title
Quality Assessment of Fruits using Deep Learning

Abstract - Quality assessment of fruits and vegetables by humans is a time-consuming process. An automated quality assessment system helps reduce human effort. In this paper, we propose automatic fruit quality detection using deep learning. We use pre-trained deep learning model, VGG16, to detect the quality of fruit. In our frame work, the quality is assessed under two categories: Fresh and Rotten. We have collected a large dataset of fruits and vegetables in each of these categories. In our work, a total 6000sample images are presented for training under two categories of quality. Experimental results show that VGG16 model achieves high training and validation accuracy of 99.55% and 99.11% respectively. Keywords - Deep Learning, Quality Assessment, Computer Vision, VGG16