YOLO Models for Fresh Fruit Classification From Digital Videos

Abstract

Identifying food freshness is a very important; it is a part of a long historical actions by humans, because fruit freshness can tell us the information about the quality of foods. With the advancement of machine learning and computer science, which will be broadly employed in factories and markets, instead of manual classification. Recognition of the freshness of food is rapidly being replaced by computers or robots. In this book chapter, the authors conduct the research work on fruit freshness detection, we make use of YOLOv6, YOLOv7, and YOLOv8 in this project to implement fruit classifications based on a variety of digital images, which can improve the efficiency and accuracy of the classification incredibly; after the classification, the output will showcase the result of fruit fresheness classification, namely, fresh, or rotten, etc. They also compare the results of different deep learning models to discover which architecture is the best one in terms of speed and accuracy. At the end of this book chapter, the authors made use of the majority vote method to combine the results of different models to get better accuracy and recall scores. To generate the final result, the authors trained the three models individually, and also propose a majority vote to get a better performance for fresh fruit detection. Compared with the previous work, this method has higher accuracy and a much faster speed. Because this one uses the clustering method to generate the final result, it will be easy for researchers to change the backbone and get a better result in the future.