Succulent-YOLO: Smart UAV-Assisted Succulent Farmland Monitoring with CLIP-Based YOLOv10 and Mamba Computer Vision

作者：Hui Li, Fan Zhao, Feng Xue, Jiaqi Wang, Yongying Liu, Yijia Chen, Qingyang Wu, Jianghan Tao, Guochen Zhang, Dianhan Xi, Jundong Chen, H. H. Kobayashi · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17132219 · 被引用次数：63

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...