Jiaqi Wang

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.

Purpose: Precise segmentation of blueberry maturity is critical for optimizing harvestschedules and maintaining product quality. Traditional methods, which rely on manualinspection, are not only labor-intensive but also cost-inefficient. This study presents a novelframework that integrates deep learning-based super-resolution reconstruction (SRR) withsemantic segmentation to provide a fast and accurate solution for maturity assessment. Methods: The SRR module enhances image resolution, enabling more detailed feature extraction.Semantic segmentation models—incorporating convolutional neural networks (CNNs),Transformer-based models, and the Mamba-based state space architecture—further improvesegmentation precision. Results: Experimental results indicate that the MambaIR modelachieves a structural similarity index measure (SSIM) of 82.26% in SRR tasks, while the Mamba-based segmentation model attains a mean Intersection over Union (mIoU) of 83.15%. Conclusion: By uniting SRR and semantic segmentation, our framework not only advances thetechnical accuracy of maturity detection but also holds strong potential for real-time, cost-effective deployment in precision agriculture systems, supporting intelligent decision-making at scale.