Performance Comparison of Cherry Tomato Ripeness Detection Using Multiple YOLO Models

Abstract

Millions of tons of cherry tomatoes are produced annually, with the harvesting process being crucial. This paper presents a deep learning-based approach to distinguish the ripeness of cherry tomatoes in real time. It specifically evaluates the performance of YOLO (You Only Look Once) v5 and YOLOv8 (with a ResNet50 backbone) models. A new dataset was created by augmenting the original 300 images to 742 images using techniques such as rotation, flipping, and brightness adjustments. Experimental results show that YOLOv8 achieved a mean average precision (mAP) of 0.757, outperforming YOLOv5, which achieved an mAP of 0.701, by 5.6%. The proposed system is expected to address labor shortages caused by population decline in rural areas and enhance productivity in cherry tomato harvesting environments. Future research will focus on integrating segmentation techniques to precisely locate cherry tomatoes and develop a robotic manipulator capable of automating the harvesting process based on ripeness. This study provides a foundation for intelligent harvesting robots applicable in real-world.