A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation

Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. 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. This framework offers a practical approach for precision floriculture and sets the stage for integrating advanced detection technologies into real-time crop monitoring systems, advancing intelligent, data-driven agriculture.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/icaace65325.2025.11020130
Primary Topic
Greenhouse Technology and Climate Control
Type
article
Field-Weighted Citation Impact
0.00

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article

A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation

Chenyu Zhang, Qingyang Wu, Jianghan Tao, Feng Xue et al.
Greenhouse Technology and Climate Control
article

A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation

Chenyu Zhang, Qingyang Wu, Jianghan Tao, Feng Xue, Shutang You, Yongying Liu, Boheng Li, Zhijie Zhang, Jiaqi Wang, Yijia Chen, Danhua Liu, Zhiyan Ren, Shan Sun, Zhiben Yin
article en

Abstract

Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. 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. This framework offers a practical approach for precision floriculture and sets the stage for integrating advanced detection technologies into real-time crop monitoring systems, advancing intelligent, data-driven agriculture.

Waseda University (JP), University of California, Los Angeles (US), Hosei University (JP), China University of Petroleum, Beijing (CN), Xinjiang Institute of Engineering (CN), Sophia University (JP), City University of Macau (MO), The University of Tokyo (JP)
Japan Science and Technology Agency
Zero hunger
Openalex Percentile: Top 91%
Greenhouse Technology and Climate Control
DOISourceOpenAlex
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A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation — Chenyu Zhang, Qingyang Wu, et al. · (2025) | TGRS Research Map | TGRS