Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring
Sicheng You, Boheng Li, Yijia Chen, Zhiyan Ren, Yongying Liu, Qingyang Wu, Jianghan Tao, Zhijie Zhang, Chenyu Zhang, Feng Xue, Yulun Chen, Guochen Zhang, Jundong Chen, Jiaqi Wang, Fan Zhao
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.