YOLOv9-GSSA model for efficient soybean seedlings and weeds detection
To monitor soybean seedlings growth in real time, an effective method for accurately identifying seedlings and removing weeds is essential. Challenges include the small size and morphological similarity of seedlings and weeds, complicating conventional detection methods. To tackle these issues, we p...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003661 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849702524465971200 |
|---|---|
| author | Baihe Liang Liangchen Hu Guangxing Liu Peng Hu Shaosheng Xu Biao Jie |
| author_facet | Baihe Liang Liangchen Hu Guangxing Liu Peng Hu Shaosheng Xu Biao Jie |
| author_sort | Baihe Liang |
| collection | DOAJ |
| description | To monitor soybean seedlings growth in real time, an effective method for accurately identifying seedlings and removing weeds is essential. Challenges include the small size and morphological similarity of seedlings and weeds, complicating conventional detection methods. To tackle these issues, we propose a real-time detection algorithm called YOLOv9-GSSA. The improved Mosaic-Dense algorithm increases object density at the model's input layer, enhancing its ability to capture detailed features. Additionally, the GSSA neck optimization module, combining GSConv and Gated Self-Attention, supports key information extraction and multi-scale feature interaction. The Swin-GSSA prediction head further utilizes spatial positional information, improving small object detection and handling overlapping occlusion. Experimental results show our model achieves a mAP of 47.5% with a detection speed of 23.42 ms per image, suitable for real-time monitoring. The enhanced model significantly improves the detection of soybean seedlings and weeds, making it a valuable tool for managing farmland effectively. This ultimately aids in precise yield estimation and decision-making in precision agriculture. |
| format | Article |
| id | doaj-art-261bfccd7bf54db79fcb3e7e6534247e |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-261bfccd7bf54db79fcb3e7e6534247e2025-08-20T03:17:38ZengElsevierSmart Agricultural Technology2772-37552025-12-011210113410.1016/j.atech.2025.101134YOLOv9-GSSA model for efficient soybean seedlings and weeds detectionBaihe Liang0Liangchen Hu1Guangxing Liu2Peng Hu3Shaosheng Xu4Biao Jie5School of Computer and Information, Anhui Normal University, Wuhu 241002, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Key Laboratory of Industrial Intelligence Data Security, Wuhu 241002, China; Corresponding author at: School of Computer and Information, Anhui Normal University, Wuhu 241002, China.School of Computer and Information, Anhui Normal University, Wuhu 241002, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Key Laboratory of Industrial Intelligence Data Security, Wuhu 241002, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Key Laboratory of Industrial Intelligence Data Security, Wuhu 241002, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu 241002, China; Anhui Provincial Key Laboratory of Industrial Intelligence Data Security, Wuhu 241002, ChinaTo monitor soybean seedlings growth in real time, an effective method for accurately identifying seedlings and removing weeds is essential. Challenges include the small size and morphological similarity of seedlings and weeds, complicating conventional detection methods. To tackle these issues, we propose a real-time detection algorithm called YOLOv9-GSSA. The improved Mosaic-Dense algorithm increases object density at the model's input layer, enhancing its ability to capture detailed features. Additionally, the GSSA neck optimization module, combining GSConv and Gated Self-Attention, supports key information extraction and multi-scale feature interaction. The Swin-GSSA prediction head further utilizes spatial positional information, improving small object detection and handling overlapping occlusion. Experimental results show our model achieves a mAP of 47.5% with a detection speed of 23.42 ms per image, suitable for real-time monitoring. The enhanced model significantly improves the detection of soybean seedlings and weeds, making it a valuable tool for managing farmland effectively. This ultimately aids in precise yield estimation and decision-making in precision agriculture.http://www.sciencedirect.com/science/article/pii/S2772375525003661GSConvMosaicObject detectionSelf-attentionYOLO |
| spellingShingle | Baihe Liang Liangchen Hu Guangxing Liu Peng Hu Shaosheng Xu Biao Jie YOLOv9-GSSA model for efficient soybean seedlings and weeds detection Smart Agricultural Technology GSConv Mosaic Object detection Self-attention YOLO |
| title | YOLOv9-GSSA model for efficient soybean seedlings and weeds detection |
| title_full | YOLOv9-GSSA model for efficient soybean seedlings and weeds detection |
| title_fullStr | YOLOv9-GSSA model for efficient soybean seedlings and weeds detection |
| title_full_unstemmed | YOLOv9-GSSA model for efficient soybean seedlings and weeds detection |
| title_short | YOLOv9-GSSA model for efficient soybean seedlings and weeds detection |
| title_sort | yolov9 gssa model for efficient soybean seedlings and weeds detection |
| topic | GSConv Mosaic Object detection Self-attention YOLO |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003661 |
| work_keys_str_mv | AT baiheliang yolov9gssamodelforefficientsoybeanseedlingsandweedsdetection AT liangchenhu yolov9gssamodelforefficientsoybeanseedlingsandweedsdetection AT guangxingliu yolov9gssamodelforefficientsoybeanseedlingsandweedsdetection AT penghu yolov9gssamodelforefficientsoybeanseedlingsandweedsdetection AT shaoshengxu yolov9gssamodelforefficientsoybeanseedlingsandweedsdetection AT biaojie yolov9gssamodelforefficientsoybeanseedlingsandweedsdetection |