Fruit classification algorithm design based on improved yolov8
Fruit classification plays a pivotal role in the fruit industry. Traditional fruit classification methods rely heavily on manual inspection, which significantly hampers the accuracy and efficiency of harvesting. The advent of deep learning algorithms has empowered robots to classify fruits more swiftly, thereby enhancing picking efficiency. In this paper, we propose a deep optimization target detection algorithm based on the YOLOv8s framework to improve the accuracy and efficiency of fruit classification.