Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring
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 YOLOv...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1607582/full |
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| _version_ | 1849419067355561984 |
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| author | Sicheng You Boheng Li Yijia Chen Zhiyan Ren Yongying Liu Qingyang Wu Jianghan Tao Zhijie Zhang Chenyu Zhang Feng Xue Yulun Chen Guochen Zhang Jundong Chen Jiaqi Wang Fan Zhao |
| author_facet | Sicheng You Boheng Li Yijia Chen Zhiyan Ren Yongying Liu Qingyang Wu Jianghan Tao Zhijie Zhang Chenyu Zhang Feng Xue Yulun Chen Guochen Zhang Jundong Chen Jiaqi Wang Fan Zhao |
| author_sort | Sicheng You |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c112928d85964fc5a2bab29a094d44a7 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-c112928d85964fc5a2bab29a094d44a72025-08-20T03:32:15ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.16075821607582Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoringSicheng You0Boheng Li1Yijia Chen2Zhiyan Ren3Yongying Liu4Qingyang Wu5Jianghan Tao6Zhijie Zhang7Chenyu Zhang8Feng Xue9Yulun Chen10Guochen Zhang11Jundong Chen12Jiaqi Wang13Fan Zhao14Faculty of Data Science, City University of Macau, Macau, Macau SAR, ChinaDepartment of Applied Informatics, Hosei University, Tokyo, JapanGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, JapanGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, JapanGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, JapanDepartment of Environmental Health Sciences, University of California, Los Angeles, Los Angeles, CA, United StatesGraduate School of Global Environmental Studies, Sophia University, Tokyo, JapanGraduate School of Global Environmental Studies, Sophia University, Tokyo, JapanGraduate School of Information, Production and Systems, Waseda University, Kitakyushu, JapanDepartment of Math and Applied Mathematics, China University of Petroleum-Beijing, Beijing, ChinaDepartment of Environmental Science, Southwest Forestry University, Kunming, ChinaGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, JapanData Science and AI Innovation Research Promotion Center, Shiga University, Hikone, JapanGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, JapanGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, JapanAccurately 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1607582/fullYOLOv11mambaprecision agriculturerose detectionUAV-based monitoring |
| spellingShingle | Sicheng You Boheng Li Yijia Chen Zhiyan Ren Yongying Liu Qingyang Wu Jianghan Tao Zhijie Zhang Chenyu Zhang Feng Xue Yulun Chen Guochen Zhang Jundong Chen Jiaqi Wang Fan Zhao Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring Frontiers in Plant Science YOLOv11 mamba precision agriculture rose detection UAV-based monitoring |
| title | Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring |
| title_full | Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring |
| title_fullStr | Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring |
| title_full_unstemmed | Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring |
| title_short | Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring |
| title_sort | rose mamba yolo an enhanced framework for efficient and accurate greenhouse rose monitoring |
| topic | YOLOv11 mamba precision agriculture rose detection UAV-based monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1607582/full |
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