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.
You, S., Li, B., Chen, Y., Ren, Z., Liu, Y., Wu, Q., Tao, J., Zhang, Z., Zhang, C., Xue, F., Chen, Y., Zhang, G., Chen, J., Wang, J., & Zhao, F. (2025). Rose-Mamba-YOLO: An enhanced framework for efficient and accurate greenhouse rose monitoring. Frontiers in Plant Science, 16, 1607582. https://doi.org/10.3389/fpls.2025.1607582
Source: Frontiers In