一种新的草莓（Fragaria x ananassa）成熟度检测及基于YOLO模型的比较评估数据集
A Novel Public Dataset for Strawberry (Fragaria x ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models
AI总结：
本文提出了一种新的草莓成熟度检测数据集，并通过YOLO模型比较测试，展示了小型和中型模型在智能农业中的高效性。
摘要：
草莓（Fragaria x ananassa）因其经济价值和营养价值而闻名于世，是一种广泛栽培的水果。在收获期间确定正确的成熟度水平对于防止生产者的损失和确保消费者获得高质量的产品至关重要。然而，传统方法，即仅凭视觉评估，可能会有主观性和较高的误差 margin。因此，需要计算机辅助系统。然而，文献中缺乏全面且对所有人开放的数据集，使得该领域的研究比较困难。在本研究中，提出了一个新且公开的草莓成熟度数据集，包含566张图片和1,201个标注对象，该数据集在土耳其两个不同的温室中，在不同光照和环境条件下准备。在使用YOLOv8、YOLOv9和YOLO11模型对数据集进行比较测试时，结果显示YOLOv9c模型的最高精度值为90.94%，而YOLO11s模型的最高召回值为83.74%。在总体性能指标mAP@50方面，YOLOv8s模型表现最佳，成功率为86.09%。结果表明，小型和中型模型在处理此类数据集时更加平衡和高效，同时也为智能农业应用建立了基础参考点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
