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.

Accurate segmentation of crops and weeds is essential for enhancing crop yield, optimizing herbicide usage, and mitigating environmental impacts. Traditional weed management practices, such as manual weeding or broad-spectrum herbicide application, are labor-intensive, environmentally harmful, and economically inefficient. In response, this study introduces a novel precision agriculture framework integrating Unmanned Aerial Vehicle (UAV)-based remote sensing with advanced deep learning techniques, combining Super-Resolution Reconstruction (SRR) and semantic segmentation. This study is the first to integrate UAV-based SRR and semantic segmentation for tobacco fields, systematically evaluate recent Transformer and Mamba-based models alongside traditional CNNs, and release an annotated dataset that not only ensures reproducibility but also provides a resource for the research community to develop and benchmark future models. Initially, SRR enhanced the resolution of low-quality UAV imagery, significantly improving detailed feature extraction. Subsequently, to identify the optimal segmentation model for the proposed framework, semantic segmentation models incorporating CNN, Transformer, and Mamba architectures were used to differentiate crops from weeds. Among evaluated SRR methods, RCAN achieved the optimal reconstruction performance, reaching a Peak Signal-to-Noise Ratio (PSNR) of 24.98 dB and a Structural Similarity Index (SSIM) of 69.48%. In semantic segmentation, the ensemble model integrating Transformer (DPT with DINOv2) and Mamba-based architectures achieved the highest mean Intersection over Union (mIoU) of 90.75%, demonstrating superior robustness across diverse field conditions. Additionally, comprehensive experiments quantified the impact of magnification factors, Gaussian blur, and Gaussian noise, identifying an optimal magnification factor of 4$\times$, proving that the method was robust to common environmental disturbances at optimal parameters. Overall, this research established an efficient, precise framework for crop cultivation management, offering valuable insights for precision agriculture and sustainable farming practices.

Accurate cattle pose estimation is important for precision livestock farming because body landmarks can provide quantitative visual inputs for potential health monitoring, behavior analysis, lameness assessment, and welfare evaluation. This study focuses on keypoint-localization performance and provides a foundation for future task-specific studies of these downstream outcomes. However, real farm images commonly contain inter-cattle occlusion, cluttered backgrounds, small anatomical landmarks, and visually similar animals, which limit the reliability of existing one-stage pose estimators. This study proposes LEC-Pose, a hierarchical locality refinement framework for occlusion-robust cattle pose estimation. Built on YOLOv8-Pose, LEC-Pose first predicts cattle boxes and auxiliary coarse keypoints, then extracts instance-level ROI features to recover local anatomical evidence. A lightweight refinement network directly predicts the final keypoints from heatmaps and offsets on enhanced ROI features. The detected boxes guide the ROI pathway, while the initial keypoints remain auxiliary outputs. During training, an instance-level contrastive loss regularizes a compact global ROI descriptor. During inference, pair construction and contrastive-loss computation are removed, while descriptor fusion remains in the prediction path. Across three runs on CattleEyeView, LEC-Pose reaches 39.47 ± 0.33 mAP@50:95, compared with 33.91 ± 0.28 for YOLOv8-Pose, while running at 111.6 FPS versus 138.2 FPS for YOLOv8-Pose, corresponding to a 19.2% throughput reduction on the evaluated RTX 4090. On NWAFU-Cattle, it obtains 76.08 ± 0.36 and 80.03 ± 0.38 mAP@50:95 under the 50%/50% and 80%/20% protocols, respectively; the former is comparable to FSMC-Pose, whereas the latter is the highest mean among the three repeated principal models. An end-to-end zero-shot evaluation on three external cattle datasets, second-dataset occlusion analysis, detailed module combinations, and descriptor diagnostics further assess the framework within their stated protocols. These results suggest that LEC-Pose can serve as a pose-estimation component for future automated cattle monitoring, and future task-specific studies can connect these keypoints to health, behavior, and welfare outcomes.

Accurate detection of blooming roses and flower buds is essential for greenhouse phenotyping, cultivation scheduling, harvest planning, and yield management. However, UAV-derived greenhouse imagery presents major challenges because rose targets are often small, densely distributed, partially occluded, and visually similar to complex backgrounds. This study proposes a lightweight rose detection framework that combines multi-source dataset construction with an improved YOLOv12n-based detector, termed BloomRoseNet. A GreenHouse Rose dataset was constructed by integrating self-collected UAV overhead images, screened RoseTracker images, and supplementary multi-view rose images to increase diversity in scale, growth stage, viewpoint, and background complexity. BloomRoseNet introduces task-oriented improvements for fine-grained feature extraction, adaptive feature fusion, and attention-enhanced detection. The supplementary multi-view data improved precision, recall, and mAP@50 from 85.2%, 82.8%, and 89.2% to 86.1%, 85.3%, and 90.5%, respectively. Compared with the baseline YOLOv12n, BloomRoseNet increased precision, recall, mAP@50, and mAP@50:95 by 3.2, 3.3, 3.6, and 1.6 percentage points, respectively, while reducing parameters from 2.55 M to 2.08 M and model size from 5.5 MB to 4.5 MB. The model also maintained real-time inference capability and stronger robustness under blur, occlusion, and illumination disturbances. The proposed framework provides an effective and practical solution for UAV-based greenhouse rose monitoring and supports precision cultivation management.