YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
Accurate tomato maturity detection represents a critical challenge in precision agriculture. A YOLOv11-based algorithm named YOLO-PGC is proposed in this study for tomato maturity detection. Its three innovative components are denoted by “PGC”, respectively representing the Polarization State Space...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5000 |
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| author | Qian Wu Heming Huang Dongke Song Jie Zhou |
| author_facet | Qian Wu Heming Huang Dongke Song Jie Zhou |
| author_sort | Qian Wu |
| collection | DOAJ |
| description | Accurate tomato maturity detection represents a critical challenge in precision agriculture. A YOLOv11-based algorithm named YOLO-PGC is proposed in this study for tomato maturity detection. Its three innovative components are denoted by “PGC”, respectively representing the Polarization State Space Strategy with Dynamic Weight Allocation, the Global Horizontal–Vertical Context Module, and the Convolutional–Inductive Feature Fusion Module. The Polarization Strategy enhances robustness against occlusion through adaptive feature importance modulation, he Global Context Module integrates cross-dimensional attention mechanisms with hierarchical feature extraction, and the Convolutional–Inductive Feature Fusion Module employs multimodal integration for improved object discrimination in complex scenes. Experimental results demonstrate that YOLO-PGC achieves superior precision and mean average precision compared to state-of-the-art methods. Validation on the COCO benchmark confirms the framework’s generalization capabilities, maintaining computational efficiency for real-time deployment. YOLO-PGC establishes new performance standards for agricultural object detection with potential applications in similar computer vision challenges. Overall, these components and strategies are integrated into YOLO-PGC to achieve robust object detection in complex scenarios. |
| format | Article |
| id | doaj-art-ab198453b040412c91aa8e4ce667cfd2 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ab198453b040412c91aa8e4ce667cfd22025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-04-01159500010.3390/app15095000YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11Qian Wu0Heming Huang1Dongke Song2Jie Zhou3The School of Computer, Qinghai Normal University, Xining 810008, ChinaThe School of Computer, Qinghai Normal University, Xining 810008, ChinaThe School of Computer, Qinghai Normal University, Xining 810008, ChinaThe School of Computer, Qinghai Normal University, Xining 810008, ChinaAccurate tomato maturity detection represents a critical challenge in precision agriculture. A YOLOv11-based algorithm named YOLO-PGC is proposed in this study for tomato maturity detection. Its three innovative components are denoted by “PGC”, respectively representing the Polarization State Space Strategy with Dynamic Weight Allocation, the Global Horizontal–Vertical Context Module, and the Convolutional–Inductive Feature Fusion Module. The Polarization Strategy enhances robustness against occlusion through adaptive feature importance modulation, he Global Context Module integrates cross-dimensional attention mechanisms with hierarchical feature extraction, and the Convolutional–Inductive Feature Fusion Module employs multimodal integration for improved object discrimination in complex scenes. Experimental results demonstrate that YOLO-PGC achieves superior precision and mean average precision compared to state-of-the-art methods. Validation on the COCO benchmark confirms the framework’s generalization capabilities, maintaining computational efficiency for real-time deployment. YOLO-PGC establishes new performance standards for agricultural object detection with potential applications in similar computer vision challenges. Overall, these components and strategies are integrated into YOLO-PGC to achieve robust object detection in complex scenarios.https://www.mdpi.com/2076-3417/15/9/5000agricultural automationtomato ripening detectionYOLOcomplex context |
| spellingShingle | Qian Wu Heming Huang Dongke Song Jie Zhou YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 Applied Sciences agricultural automation tomato ripening detection YOLO complex context |
| title | YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 |
| title_full | YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 |
| title_fullStr | YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 |
| title_full_unstemmed | YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 |
| title_short | YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 |
| title_sort | yolo pgc a tomato maturity detection algorithm based on improved yolov11 |
| topic | agricultural automation tomato ripening detection YOLO complex context |
| url | https://www.mdpi.com/2076-3417/15/9/5000 |
| work_keys_str_mv | AT qianwu yolopgcatomatomaturitydetectionalgorithmbasedonimprovedyolov11 AT heminghuang yolopgcatomatomaturitydetectionalgorithmbasedonimprovedyolov11 AT dongkesong yolopgcatomatomaturitydetectionalgorithmbasedonimprovedyolov11 AT jiezhou yolopgcatomatomaturitydetectionalgorithmbasedonimprovedyolov11 |