AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes
IntroductionTo achieve fast detection of pear fruits in natural pear orchards and optimize path planning for harvesting robots, this study proposes the AHG-YOLO model for multi-category detection of pear fruit occlusion in complex orchard environments.MethodsUsing the Red Delicious pear as the resea...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1580325/full |
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| author | Na Ma Na Ma Na Ma Yile Sun Yile Sun Chenfei Li Chenfei Li Zonglin Liu Zonglin Liu Haiyan Song Haiyan Song |
| author_facet | Na Ma Na Ma Na Ma Yile Sun Yile Sun Chenfei Li Chenfei Li Zonglin Liu Zonglin Liu Haiyan Song Haiyan Song |
| author_sort | Na Ma |
| collection | DOAJ |
| description | IntroductionTo achieve fast detection of pear fruits in natural pear orchards and optimize path planning for harvesting robots, this study proposes the AHG-YOLO model for multi-category detection of pear fruit occlusion in complex orchard environments.MethodsUsing the Red Delicious pear as the research object, the pears are classified into three categories based on different occlusion statuses: non-occluded fruits (NO), fruits occluded by leaves/branches (OBL), and fruits in close contact with other fruits but not obstructed by leaves/branches (FCC). The YOLOv11n model is used as the base model for a lightweight design. First, the sampling method in the backbone and neck networks is replaced with ADown downsampling to capture higher-level image features, reducing floating-point operations and computational complexity. Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. Finally, the boundary box loss function is changed to Generalized Intersection over Union (GIoU), improving the model’s convergence speed and further enhancing detection performance.ResultsExperimental results show that the AHG-YOLO model achieves 93.5% (FCC), 95.3% (NO), and 93.4% (OBL) in AP, with an mAP@0.5 of 94.1% across all categories. Compared to the base YOLOv11n network, precision, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 2.5%, 3.6%, 2.3%, and 2.6%, respectively. The model size is only 5.1MB, with a 16.9% reduction in the number of parameters.DiscussionThe improved model demonstrates enhanced suitability for deployment on pear-harvesting embedded devices, providing technical support for the path planning of fruit-picking robotic arms. |
| format | Article |
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| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj-art-2ce3933ebc484badab681db02b2031642025-08-20T02:25:45ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15803251580325AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenesNa Ma0Na Ma1Na Ma2Yile Sun3Yile Sun4Chenfei Li5Chenfei Li6Zonglin Liu7Zonglin Liu8Haiyan Song9Haiyan Song10College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, ChinaDryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong, ChinaCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, ChinaDryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, ChinaDryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, ChinaDryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, ChinaDryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong, ChinaIntroductionTo achieve fast detection of pear fruits in natural pear orchards and optimize path planning for harvesting robots, this study proposes the AHG-YOLO model for multi-category detection of pear fruit occlusion in complex orchard environments.MethodsUsing the Red Delicious pear as the research object, the pears are classified into three categories based on different occlusion statuses: non-occluded fruits (NO), fruits occluded by leaves/branches (OBL), and fruits in close contact with other fruits but not obstructed by leaves/branches (FCC). The YOLOv11n model is used as the base model for a lightweight design. First, the sampling method in the backbone and neck networks is replaced with ADown downsampling to capture higher-level image features, reducing floating-point operations and computational complexity. Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. Finally, the boundary box loss function is changed to Generalized Intersection over Union (GIoU), improving the model’s convergence speed and further enhancing detection performance.ResultsExperimental results show that the AHG-YOLO model achieves 93.5% (FCC), 95.3% (NO), and 93.4% (OBL) in AP, with an mAP@0.5 of 94.1% across all categories. Compared to the base YOLOv11n network, precision, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 2.5%, 3.6%, 2.3%, and 2.6%, respectively. The model size is only 5.1MB, with a 16.9% reduction in the number of parameters.DiscussionThe improved model demonstrates enhanced suitability for deployment on pear-harvesting embedded devices, providing technical support for the path planning of fruit-picking robotic arms.https://www.frontiersin.org/articles/10.3389/fpls.2025.1580325/fullYOLOv11pear fruitsobject detectionADowngroup convolutionGIoU |
| spellingShingle | Na Ma Na Ma Na Ma Yile Sun Yile Sun Chenfei Li Chenfei Li Zonglin Liu Zonglin Liu Haiyan Song Haiyan Song AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes Frontiers in Plant Science YOLOv11 pear fruits object detection ADown group convolution GIoU |
| title | AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes |
| title_full | AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes |
| title_fullStr | AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes |
| title_full_unstemmed | AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes |
| title_short | AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes |
| title_sort | ahg yolo multi category detection for occluded pear fruits in complex orchard scenes |
| topic | YOLOv11 pear fruits object detection ADown group convolution GIoU |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1580325/full |
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