GE-YOLO for Weed Detection in Rice Paddy Fields
Weeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, an...
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MDPI AG
2025-03-01
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| author | Zimeng Chen Baifan Chen Yi Huang Zeshun Zhou |
| author_facet | Zimeng Chen Baifan Chen Yi Huang Zeshun Zhou |
| author_sort | Zimeng Chen |
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| description | Weeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. The current research has yet to address these issues adequately. Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. Multiple experiments demonstrate that GE-YOLO achieves 93.1% mAP, 90.3% F1 Score, and 85.9 FPS, surpassing almost all mainstream object detection algorithms such as YOLOv8, YOLOv10, and YOLOv11 in terms of detection accuracy and overall performance. Furthermore, the detection results under different lighting conditions consistently maintained a high level above 90% mAP, and under conditions of heavy occlusion, the average mAP for all weed types reached 88.7%. These results indicate that GE-YOLO has excellent detection accuracy and generalization performance, highlighting the potential of GE-YOLO as a valuable tool for enhancing weed management practices in rice cultivation. |
| format | Article |
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| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-940fbb08292c413a8773ad0a4ebc9d3b2025-08-20T02:57:41ZengMDPI AGApplied Sciences2076-34172025-03-01155282310.3390/app15052823GE-YOLO for Weed Detection in Rice Paddy FieldsZimeng Chen0Baifan Chen1Yi Huang2Zeshun Zhou3School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaWeeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. The current research has yet to address these issues adequately. Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. Multiple experiments demonstrate that GE-YOLO achieves 93.1% mAP, 90.3% F1 Score, and 85.9 FPS, surpassing almost all mainstream object detection algorithms such as YOLOv8, YOLOv10, and YOLOv11 in terms of detection accuracy and overall performance. Furthermore, the detection results under different lighting conditions consistently maintained a high level above 90% mAP, and under conditions of heavy occlusion, the average mAP for all weed types reached 88.7%. These results indicate that GE-YOLO has excellent detection accuracy and generalization performance, highlighting the potential of GE-YOLO as a valuable tool for enhancing weed management practices in rice cultivation.https://www.mdpi.com/2076-3417/15/5/2823weed detectionYOLOv8Gold-YOLOEMAGIOU loss |
| spellingShingle | Zimeng Chen Baifan Chen Yi Huang Zeshun Zhou GE-YOLO for Weed Detection in Rice Paddy Fields Applied Sciences weed detection YOLOv8 Gold-YOLO EMA GIOU loss |
| title | GE-YOLO for Weed Detection in Rice Paddy Fields |
| title_full | GE-YOLO for Weed Detection in Rice Paddy Fields |
| title_fullStr | GE-YOLO for Weed Detection in Rice Paddy Fields |
| title_full_unstemmed | GE-YOLO for Weed Detection in Rice Paddy Fields |
| title_short | GE-YOLO for Weed Detection in Rice Paddy Fields |
| title_sort | ge yolo for weed detection in rice paddy fields |
| topic | weed detection YOLOv8 Gold-YOLO EMA GIOU loss |
| url | https://www.mdpi.com/2076-3417/15/5/2823 |
| work_keys_str_mv | AT zimengchen geyoloforweeddetectioninricepaddyfields AT baifanchen geyoloforweeddetectioninricepaddyfields AT yihuang geyoloforweeddetectioninricepaddyfields AT zeshunzhou geyoloforweeddetectioninricepaddyfields |