Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB
Comparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. The model achieves precision, recall, and mean Average Precision of 94.7%, 93.0%, and 97.0%, respectively, significantly reducing false negatives and false positives. With a file size of 3.2 MB, 4.1 GFLOPs, and a frame rate of 143 FPS, YOLO11-CGB model is designed to meet the operational demands of edge devices, offering a robust solution for precision spraying technology in agriculture.
Authors
- Miao Wu (ORCID: https://orcid.org/0000-0002-3320-4267)
- Hang Shi (ORCID: https://orcid.org/0000-0003-2330-7438)
- Jun Hu (ORCID: https://orcid.org/0000-0003-2546-8403)
- Changxi Liu (ORCID: https://orcid.org/0000-0002-0277-3475)
- Yufei Li (ORCID: https://orcid.org/0000-0003-2783-6468)
- Hao Sun (ORCID: https://orcid.org/0000-0003-0951-6428)
- Hang Song (ORCID: https://orcid.org/0000-0002-6470-0976)
- Hui Zhang (ORCID: https://orcid.org/0000-0003-4683-8645)
Institutions
- Heilongjiang Bayi Agricultural University (CN)
- Education Department of Heilongjiang Province (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-04-03
- DOI
- https://doi.org/10.3389/fpls.2025.1558378
- Citations
- 10
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 17.50