دانلود گزارش IEEE DataPort : Multi-Class Strawberry Ripeness Detection Dataset - 2026
TXT, JPG نویسندگان: Mustafa Yurdakul
جزئیات
فرمت: TXT, JPG ناشر: IEEE DataPort تاریخ انتشار نسخه الکترونیکی : 02/20/2026
؟
شابک: 10.21227/dmwm-sc17
1,499,000 تومان
299,800 تومانبن تخفیف
زمان تحویل: آنی
توضیحات
This dataset presents a publicly available strawberry ripeness detection benchmark designed for object detection and smart agriculture research. It contains annotated images of Fragaria × ananassa collected from two different greenhouse environments in Türkiye under variable lighting conditions, including direct sunlight, partial shading, and diffuse greenhouse illumination.The dataset was created to support: ???? Multi-class ripeness detection ???? Real-time object detection model development (YOLO-based systems) ???? Smart farming and autonomous harvesting research ???? Fair and reproducible benchmarking across architectures???? 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.???? Reference Yurdakul, M., Baştuğ, Z. S., Gök, A. E., & Taşdemir, Ş. A Novel Public Dataset for Strawberry (Fragaria × ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models.https://arxiv.org/abs/2602.15656v2
1,499,000 تومان
299,800 تومانبن تخفیف
زمان تحویل: آنی