| Weed identification method in soybean\|corn intercropping systems based on CSM-YOLO |
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| DOI：10.7606/j.issn.1000-7601.2025.06.24 |
| Key Words: weed identification soybean and maize compound planting instance segmentation improved YOLOv8n-seg deep learning |
| Author Name | Affiliation | | ZHU Huibin | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China | | FANG Yuan | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China | | BAI Lizhen | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China | | WANG Mingpeng | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China | | LI Shi | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China | | LI Rongdong | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China |
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| Abstract: |
| To address the challenges of diverse weeds and high morphological similarity between crops and weeds in intercropping systems in southwestern China, an improved CSM-YOLO model based on YOLOv8n-seg was proposed. The model incorporates a CA attention mechanism to enhance geometric feature extraction, SCConv convolution to suppress background noise, and Matrix NMS to accelerate inference. A “crop\|environment\|weed” coupled image dataset was constructed for validation. The results showed that the model achieves a 0.1%~1.3% improvement in weed recognition accuracy compared to the baseline model, with an inference speed of 197.36 FPS. The model's precision, mAP@0.5, and recall rate reached 92.9%, 94.0%, and 90.7%, respectively, outperforming mainstream models. Visualization results further confirmed its superior performance, particularly in small\|target weed detection tasks, with heatmaps indicating highly selective attention to crop and weed regions. This study provides efficient technical support for weed recognition in soybean\|corn intercropping systems. |
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