The strawberry (Fragaria x ananassa), known worldwide for its economic value and nutritional richness, is a widely cultivated fruit. Determining the correct ripeness level during the harvest period is crucial for both preventing losses for producers and ensuring consumers receive a quality product. However, traditional methods, i.e., visual assessments alone, can be subjective and have a high margin of error. Therefore, computer-assisted systems are needed. However, the scarcity of comprehensive datasets accessible to everyone in the literature makes it difficult to compare studies in this field. In this study, a new and publicly available strawberry ripeness dataset, consisting of 566 images and 1,201 labeled objects, prepared under variable light and environmental conditions in two different greenhouses in Turkey, is presented to the literature. Comparative tests conducted on the data set using YOLOv8, YOLOv9, and YOLO11-based models showed that the highest precision value was 90.94% in the YOLOv9c model, while the highest recall value was 83.74% in the YOLO11s model. In terms of the general performance criterion mAP@50, YOLOv8s was the best performing model with a success rate of 86.09%. The results show that small and medium-sized models work more balanced and efficiently on this type of dataset, while also establishing a fundamental reference point for smart agriculture applications.

翻译：草莓（Fragaria x ananassa）以其经济价值和营养丰富性闻名全球，是一种广泛种植的水果。在采收期准确判断成熟度对于防止生产者损失和确保消费者获得优质产品至关重要。然而，传统方法（即仅依靠视觉评估）具有主观性且误差较大。因此，需要计算机辅助系统。但现有文献中缺乏可供所有人使用的综合性数据集，导致该领域研究难以进行有效比较。本研究向学术界提供了一个新颖的公开草莓成熟度数据集，该数据集包含566张图像和1,201个标注对象，采集自土耳其两个不同温室中变化的光照与环境条件。使用基于YOLOv8、YOLOv9和YOLO11的模型在数据集上进行的对比测试表明：YOLOv9c模型获得了最高的精确率（90.94%），而YOLO11s模型则取得了最高的召回率（83.74%）。在综合性能指标mAP@50方面，YOLOv8s以86.09%的成功率成为性能最佳的模型。结果表明，中小型模型在此类数据集上运行更为平衡高效，同时也为智慧农业应用建立了基础参考基准。