Investigating YOLO Models for Rice Seed Classification

Abstract

Rice is an important staple food over the world. The purity of rice seed is one of the main factors affecting rice quality and yield. Traditional methods of assessing the purity of rice varieties depend on the decision of human technicians/experts. This work requires a considerable amount of time and cost as well as can lead to unreliable results. To overcome these problems, this study investigates YOLO models for the automated classification of rice varieties. Experiments on an image dataset of six popular rice varieties in Vietnam demonstrate that the YOLOv5 model outperforms the other YOLO variants in both accuracy and time of training model.