"Multi-Class Strawberry Ripeness Detection Dataset"

"This dataset presents a publicly available strawberry ripeness detection benchmark designed for object detection and smart agriculture research. It contains annotated images of Fragaria \u00d7 ananassa collected from two different greenhouse environments in T\u00fcrkiye under variable lighting conditions, including direct sunlight, partial shading, and diffuse greenhouse illumination.The dataset was created to support: \ud83c\udf53 Multi-class ripeness detection \ud83e\udd16 Real-time object detection model development (YOLO-based systems) \ud83c\udf31 Smart farming and autonomous harvesting research \ud83d\udcca Fair and reproducible benchmarking across architectures\ud83d\udccc Citation Requirement This dataset is introduced in the following research article. If you use this dataset in any academic publication, thesis, project, or derivative work, citation of the following paper is mandatory.\ud83d\udcd6 Reference Yurdakul, M., Ba\u015ftu\u011f, Z. S., G\u00f6k, A. E., & Ta\u015fdemir, \u015e. A Novel Public Dataset for Strawberry (Fragaria \u00d7 ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models.https:\/\/arxiv.org\/abs\/2602.15656v2"