Recent advances in unmanned aerial vehicle (UAV) technology combined with deep learning techniques have greatly improved agricultural monitoring. However, accurately processing images at low resolutions remains challenging for precision cultivation of succulents. To address this issue, this study proposes a novel method that combines cutting-edge super-resolution reconstruction (SRR) techniques with object detection and then applies the above model in a unified drone framework to achieve large-scale, reliable monitoring of succulent plants. Specifically, we introduce MambaIR, an innovative SRR method leveraging selective state-space models, significantly improving the quality of UAV-captured low-resolution imagery (achieving a PSNR of 23.83 dB and an SSIM of 79.60%) and surpassing current state-of-the-art approaches. Additionally, we develop Succulent-YOLO, a customized target detection model optimized for succulent image classification, achieving a mean average precision (mAP@50) of 87.8% on high-resolution images. The integrated use of MambaIR and Succulent-YOLO achieves an mAP@50 of 85.1% when tested on enhanced super-resolution images, closely approaching the performance on original high-resolution images. Through extensive experimentation supported by Grad-CAM visualization, our method effectively captures critical features of succulents, identifying the best trade-off between resolution enhancement and computational demands. By overcoming the limitations associated with low-resolution UAV imagery in agricultural monitoring, this solution provides an effective, scalable approach for evaluating succulent plant growth. Addressing image-quality issues further facilitates informed decision-making, reducing technical challenges. Ultimately, this study provides a robust foundation for expanding the practical use of UAVs and artificial intelligence in precision agriculture, promoting sustainable farming practices through advanced remote sensing technologies.

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.