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Peer-reviewedCC BY 4.0Frontiers in Plant Science

Complex scene green walnut target detection method based on improved YOLOv11n

  • Tingfeng Li1,2
  • Xuanxi Yang3
  • Ruoyu Li1,2
  • Chen Xue1,2
  • Zaiqing Chen1,2
  • Lijun Yun1,2
  1. 1School of Information, Yunnan Normal University
  2. 2Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province
  3. 3Center for Planning and Policy Research, Yunnan Institute of Forest Inventory and Planning
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Abstract

To meet the efficiency and accuracy requirements of agricultural product inspection, deep learning-based object detection technology has been widely adopted. However, factors such as varying lighting conditions and foliage occlusion cause green walnuts in complex scenes to exhibit low texture contrast and a tendency to lose edge information. To address these issues, this paper proposes GPL-YOLO, an object detection algorithm for green walnuts based on an improved YOLOv11n architecture. First, the GhostModuleV2 module is introduced into the backbone network to enhance global modeling capabilities and enrich the detailed information of walnut targets within high-level feature maps. Second, a PPA module is integrated into the multi-scale feature fusion stage of the neck; this module effectively filters and enhances fused features across spatial and channel dimensions, thereby highlighting the target regions of green walnuts while suppressing background noise. Finally, a DW-LAE feature enhancement module is embedded in the downsampling fusion branch to adaptively preserve and enhance shallow-level detailed features during downsampling, thereby minimizing the loss of edge, texture, and shape information and improving the effectiveness of deep semantic features. Experimental results demonstrate that, compared to the baseline model, this algorithm achieves improvements of 8.6%, 5.7%, and 8.7% in Precision, Recall, and mAP50, respectively. The improved model outperforms the original YOLOv11n in detection accuracy and effectively mitigates missed detections and false positives in complex backgrounds, providing technical support for intelligent harvesting, yield estimation, and precision management in green walnut orchards.

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Cite

BibTeXRIS
BibTeX
@article{li2026complex,
  title = {{Complex scene green walnut target detection method based on improved YOLOv11n}},
  author = {Tingfeng Li and Xuanxi Yang and Ruoyu Li and Chen Xue and Zaiqing Chen and Lijun Yun},
  journal = {Frontiers in Plant Science},
  year = {2026},
  month = oct,
  doi = {10.3389/fpls.2026.1971272},
  url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1971272/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Complex scene green walnut target detection method based on improved YOLOv11n
AU  - Tingfeng Li
AU  - Xuanxi Yang
AU  - Ruoyu Li
AU  - Chen Xue
AU  - Zaiqing Chen
AU  - Lijun Yun
PY  - 2026
DA  - 2026/10/14
JO  - Frontiers in Plant Science
DO  - 10.3389/fpls.2026.1971272
UR  - https://www.frontiersin.org/articles/10.3389/fpls.2026.1971272/full
AB  - To meet the efficiency and accuracy requirements of agricultural product inspection, deep learning-based object detection technology has been widely adopted. However, factors such as varying lighting conditions and foliage occlusion cause green walnuts in complex scenes to exhibit low texture contrast and a tendency to lose edge information. To address these issues, this paper proposes GPL-YOLO, an object detection algorithm for green walnuts based on an improved YOLOv11n architecture. First, the GhostModuleV2 module is introduced into the backbone network to enhance global modeling capabilities and enrich the detailed information of walnut targets within high-level feature maps. Second, a PPA module is integrated into the multi-scale feature fusion stage of the neck; this module effectively filters and enhances fused features across spatial and channel dimensions, thereby highlighting the target regions of green walnuts while suppressing background noise. Finally, a DW-LAE feature enhancement module is embedded in the downsampling fusion branch to adaptively preserve and enhance shallow-level detailed features during downsampling, thereby minimizing the loss of edge, texture, and shape information and improving the effectiveness of deep semantic features. Experimental results demonstrate that, compared to the baseline model, this algorithm achieves improvements of 8.6%, 5.7%, and 8.7% in Precision, Recall, and mAP50, respectively. The improved model outperforms the original YOLOv11n in detection accuracy and effectively mitigates missed detections and false positives in complex backgrounds, providing technical support for intelligent harvesting, yield estimation, and precision management in green walnut orchards.
SN  - 1664-462X
ER  -