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GIA-YOLO: A Target Detection Method for Nectarine Picking Robots in Facility Orchards

文献类型： 外文期刊

作者： Ren, Longlong 1 ; Li, Yuqiang 1 ; Du, Yonghui 1 ; Gao, Ang 1 ; Ma, Wei 3 ; Song, Yuepeng 1 ; Han, Xingchang 4 ;

作者机构： 1.Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271018, Peoples R China

2.Shandong Key Lab Intelligent Prod Technol & Equipm, Tai An 271018, Peoples R China

3.Chinese Acad Agr Sci, Inst Urban Agr, Chengdu 610213, Peoples R China

4.Shandong Acad Agr Machinery Sci, Jinan 250100, Peoples R China

5.Minist Agr & Rural Affairs, Huang Huai Hai Lab Modern Agr Equipment, Jinan 250100, Peoples R China

关键词： deep learning; nectarine; target detection; facility orchard; picking robotics

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 8 期

页码：

收录情况： SCI

摘要： The complex and variable environment of facility orchards poses significant challenges for intelligent robotic operations. To address issues such as nectarine fruit occlusion by branches and leaves, complex backgrounds, and the demand for high real-time detection performance, this study proposes a target detection model for nectarine fruit based on the YOLOv11 architecture-Ghost-iEMA-ADown You Only Look (GIA-YOLO). We introduce the GhostModule to reduce the model size and the floating-point operations, adopt the fusion attention mechanism iEMA to enhance the feature extraction capability, and further optimize the network structure through the ADown lightweight downsampling module. The test results show that GIA-YOLO achieves 93.9% precision, 88.9% recall, and 96.2% mAP, which are 2.2, 1.1, and 0.7 percentage points higher than YOLOv11, respectively; the size of the model is reduced to 5.0 MB and the floating-point operations is reduced to 5.2 G, which is 9.1% and 17.5% less compared to the original model, respectively. The model was deployed in the picking robot system and field tested in the nectarine facility orchard, the results show that GIA-YOLO maintains high detection precision and stability at different picking distances, with a comprehensive missed detection rate of 6.65%, a false detection rate of 8.7%, and supports real-time detection at 41.6 FPS. The results of the research provide an important reference and support for the optimization of the design and application of the nectarine detection model in the facility agriculture environment.

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