A Scheme Based on Deep Learning for Fruit Classification

Abstract

Grading and classifying fruits are critical due to automated machine learning systems. In computer vision, different fruits have large complexity and similarity to identify the fruit types. In this study, we developed an efficient and reliable fruit grading system. It is very difficult to classify fruits from images with established conventional approaches. We used a Convolutional Neural Network (CNN) methodology involving comparing a custom-built CNN and the VGG pre-trained models. In the research results, the VGG model accuracy is of 99.98 percent. This research proved the effectiveness of the deep model in the challenges of fruit classification and set a foundation for its application in automated grading systems.