数字农科院2.0

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;Li, Yuqiang;Du, Yonghui;Gao, Ang;Ma, Wei;Song, Yuepeng;Han, Xingchang

作者机构：

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

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2025 年 15 卷 8 期

页码：

收录情况： SCIE（2025版）

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.

分类号：

  • 相关文献

[1]Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees. Gao, Ang,Tian, Zhiwei,Ma, Wei,Song, Yuepeng,Ren, Longlong,Feng, Yuliang,Qian, Jianping,Xu, Lijia. 2024

[2]Lightweight Structure and Attention Fusion for In-Field Crop Pest and Disease Detection. Zijing Luo,Yunsen Liang,Naimin Kong,Lirui Liang,Wenjun Peng,Yujie Yao,Chi Qin,Xiaohan Lu,Mingman Xu,Yining Zhang,Chenyang Lin,Chengyao Jiang,Mengyao Li,Yangxia Zheng,Yameng Jiang,Wei Lu. 2025

[3]Characterization of 1-aminocyclopropane-1-carboxylic acid synthase (ACS) genes during nectarine fruit development and ripening. Zeng, Wenfang,Pan, Lei,Liu, Hui,Niu, Liang,Lu, Zhenhua,Cui, Guochao,Wang, Zhiqiang. 2015

[4]The MYB transcription factor PpMYB5 regulates Pp4CL1/Pp4CL2 expression to promote lignin biosynthesis of fruit russeting in the flat nectarine. Yule Miao,Wenyi Duan,Ang Li,Mingzhu Yuan,Junren Meng,Hongmei Wang,Lei Pan,Shihang Sun,Guochao Cui,Caiyun Shi,Liang Niu,Wenfang Zeng. 2024

[5]A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS. Wang C.,Sun S.,Zhao C.,Mao Z.,Wu H.,Teng G.. 2022

[6]Diseased Fish Detection in the Underwater Environment Using an Improved YOLOV5 Network for Intensive Aquaculture. Zhen Wang,Haolu Liu,Guangyue Zhang,Xiao Yang,Lingmei Wen,Wei Zhao. 2023

[7]Key Technologies for an Orchard Variable-Rate Sprayer: Current Status and Future Prospects. Wei Z.,Xue X.,Salcedo R.,Zhang Z.,Gil E.,Sun Y.,Li Q.,Shen J.,He Q.,Dou Q.,Zhang Y.. 2023

[8]ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field. Wu, Wendi,Zhang, Jianhua,Zhou, Guomin,Zhang, Yuhang,Wang, Jian,Hu, Lin. 2024

[9]CSF-YOLO: A Lightweight Model for Detecting Grape Leafhopper Damage Levels. Chaoxue Wang,Leyu Wang,Gang Ma,Liang Zhu. 2025

[10]Method for Dairy Cow Target Detection and Tracking Based on Lightweight YOLO v11. Li, Zhongkun,Cheng, Guodong,Yang, Lu,Han, Shuqing,Wang, Yali,Dai, Xiaofei,Fang, Jianyu,Wu, Jianzhai. 2025

[11]Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method. Liu, Yilin,Han, Xiang,Ren, Longlong,Ma, Wei,Liu, Baoyou,Sheng, Changrong,Song, Yuepeng,Li, Qingda. 2025

[12]Identification of leaves of wild Ussurian Pear (Pyrus ussuriensis) based on YOLOv10n-MCS. Niman Li,Xingguang Dong,Yongqing Wu,Luming Tian,Ying Zhang,Hongliang Huo,Dan Qi,Jiayu Xu,Chao Liu,Zhiyan Chen,Yulu Mou. 2025

[13]Deep learning based weed detection and target spraying robot system at seedling stage of cotton field. Xiangpeng Fan,Xiujuan Chai,Jianping Zhou,Tan Sun. 2023

[14]Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability. Lingbo Yang,Ran Huang,Jingcheng Zhang,Jingfeng Huang,Limin Wang,Jiancong Dong,Jie Shao. 2023

[15]Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping. Yang, Lingbo,Huang, Ran,Huang, Jingfeng,Lin, Tao,Wang, Limin,Mijiti, Ruzemaimaiti,Wei, Pengliang,Tang, Chao,Shao, Jie,Li, Qiangzi,Du, Xin. 2021

[16]A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence. Ruyu Mei,Kebiao Mao,Jiancheng Shi,Jeffrey Nielson,Sayed M. Bateni,Fei Meng,Guoming Du. 2023

[17]Machine learning for image-based multi-omics analysis of leaf veins. Yubin Zhang,Ning Zhang,Xiujuan Chai,Tan Sun. 2023

[18]Auction-based deep learning-driven smart agricultural supply chain mechanism. Yu Feng,Dong Mei,Hua Zhao. 2023

[19]Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Xin Xu,Jing Shi,Yongqin Chen,Qiang He,Liangliang Liu,Tong Sun,Ruifeng Ding,Yanhui Lu,Chaoqun Xue,Hongbo Qiao. 2023

[20]Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Liangliang Zhang,Zhao Zhang,Yuchuan Luo,Juan Cao,Ruizhi Xie,Shaokun Li. 2021

作者其他论文 更多>>