A Novel Public Dataset for Strawberry (Fragaria x ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models

• URL: http://arxiv.org/abs/2602.15656v3
• Date: Mon, 23 Feb 2026 16:21:27 GMT
• Title: A Novel Public Dataset for Strawberry (Fragaria x ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models
• Authors: Mustafa Yurdakul, Zeynep Sena Bastug, Ali Emre Gok, Sakir Taşdemir,
• Abstract summary: This study presents a new and publicly available strawberry ripeness dataset, consisting of 566 images and 1,201 labeled objects.<n>The highest precision value was 90.94% in the YOLOv8c model, while the highest recall value was 83.74% in the YOLO11s model.<n>The results show that small and medium-sized models work more balanced and efficiently on this type of dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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