UAV-Assisted Succulent Farmland Monitoring with Improved YOLOv10 and MambaIR
Succulent plant image categorization suffers from subjectivity and limited number of available images, and these difficulties are intensified by the apparent lack of field surveys. In this study, we present a new approach to classify succulents using consumer-grade Unmanned Aerial Vehicles (UAVs) and deep learning. We applied various super-resolution algorithms with different image enhancement design strategies. In addition, we develop a new object detection model based on MambaIR and YOLOv10, achieving a mean average precision (mAP) of 0.851, outperforming other state-of-the-art object detection algorithms. The application of UAV s and super-resolution technology greatly facilitates the detection of succulents and provides a practical solution for precision crop monitoring.
Authors
- Hui Li (ORCID: https://orcid.org/0000-0001-6547-888X)
- Jianghan Tao (ORCID: https://orcid.org/0000-0002-0892-836X)
- Xue Feng (ORCID: https://orcid.org/0000-0002-9057-1549)
- Yongying Liu (ORCID: https://orcid.org/0000-0001-6369-4589)
- Mowen Zhang
- Zhijie Zhang (ORCID: https://orcid.org/0000-0002-7276-5649)
- Jiaqi Wang (ORCID: https://orcid.org/0000-0002-5585-2513)
- Chenyu Zhang (ORCID: https://orcid.org/0000-0002-7422-7027)
- Guocheng Zhang (ORCID: https://orcid.org/0000-0002-2085-9416)
- Yijia Chen (ORCID: https://orcid.org/0009-0006-6986-6736)
- Danhua Liu (ORCID: https://orcid.org/0000-0001-6823-6691)
- Yinyin He
- Dianhan Xi
- Qingyang Wu
Institutions
- Hong Kong Polytechnic University (HK)
- Waseda University (JP)
- China University of Petroleum, Beijing (CN)
- Sophia University (JP)
- The University of Tokyo (JP)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1109/icaace65325.2025.11019871
- Citations
- 2
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.60