AUTHOR=You Sicheng , Li Boheng , Chen Yijia , Ren Zhiyan , Liu Yongying , Wu Qingyang , Tao Jianghan , Zhang Zhijie , Zhang Chenyu , Xue Feng , Chen Yulun , Zhang Guochen , Chen Jundong , Wang Jiaqi , Zhao Fan 

TITLE=Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1607582

DOI=10.3389/fpls.2025.1607582

ISSN=1664-462X

ABSTRACT=Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. To address these issues, this study introduces ROSE-MAMBA-YOLO, a hybrid detection framework that combines the efficiency of YOLOv11 with Mamba-inspired state-space modeling to enhance feature extraction, multi-scale fusion, and contextual representation. The model achieves a mAP@50 of 87.5%, precision of 90.4%, and recall of 83.1%, surpassing state-of-the-art object detection models. Extensive evaluations validate its robustness against degraded input data and adaptability across diverse datasets. These results demonstrate the applicability of ROSE-MAMBA-YOLO in complex agricultural scenarios. With its lightweight design and real-time capability, the framework provides a scalable and efficient solution for UAV-based rose monitoring, and offers a practical approach for precision floriculture. It sets the stage for integrating advanced detection technologies into real-time crop monitoring systems, advancing intelligent, data-driven agriculture.