YOLO11s-RFBS: A Real-Time Detection Model for Kiwiberry Flowers in Complex Orchard Natural Environments

Zhedong Xie, Yuxuan Liu, Chao Zhang, Yingbo Li, Bing Tian, Yulin Fu, Jun Ai () and Hongyu Guo ()
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Zhedong Xie: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Yuxuan Liu: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Chao Zhang: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Yingbo Li: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Bing Tian: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Yulin Fu: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Jun Ai: College of Horticulture, Jilin Agricultural University, Changchun 130118, China
Hongyu Guo: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 21, 1-31

Abstract: The pollination of kiwiberry flowers is closely related to fruit growth, development, and yield. Rapid and precise identification of flowers under natural field conditions plays a key role in enhancing pollination efficiency and improving overall fruit quality. Flowers and buds are densely distributed, varying in size, and exhibiting similar colors. Complex backgrounds, lighting variations, and occlusion further challenge detection. To address these issues, the YOLO11s-RFBS model was proposed. The P5 detection head was replaced with P2 to improve the detection of densely distributed small flowers and buds. RFAConv was incorporated into the backbone to strengthen feature discrimination across multiple receptive field scales and to mitigate issues caused by parameter sharing. The C3k2-Faster module was designed to reduce redundant computation and improve feature extraction efficiency. A weighted bidirectional feature pyramid slim neck network was constructed with a compact architecture to achieve superior multi-scale feature fusion with minimal parameter usage. Experimental evaluations indicated that YOLO11s-RFBS reached a mAP@0.5 of 91.7%, outperforming YOLO11s by 2.7%, while simultaneously reducing the parameter count and model footprint by 33.3% and 31.8%, respectively. Compared with other mainstream models, it demonstrated superior comprehensive performance. Its detection speed exceeded 21 FPS in deployment, satisfying real-time requirements. In conclusion, YOLO11s-RFBS enables accurate and efficient detection of kiwiberry flowers and buds, supporting intelligent pollination robots.

Keywords: YOLO11; kiwiberry flower; BIFPSNN; real-time detection; intelligent pollination (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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