| 朱惠斌,方圆,白丽珍,王明鹏,李仕,李镕东.基于CSM-YOLO的大豆玉米复合种植模式下的杂草识别方法[J].干旱地区农业研究,2025,(6):248~258 |
| 基于CSM-YOLO的大豆玉米复合种植模式下的杂草识别方法 |
| 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 |
| 中文关键词: 杂草识别 大豆玉米复合种植 实例分割 改进YOLOv8n-seg 深度学习 |
| 英文关键词:weed identification soybean and maize compound planting instance segmentation improved YOLOv8n-seg deep learning |
| 基金项目:云南省自然科学基金项目（202401AS070115）；国家自然科学基金项目（52265033,51865022） |
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| 中文摘要: |
| 针对西南地区多作物套种场景中杂草多样、作物-杂草形态相似等挑战，提出基于YOLOv8n-seg改进的CSM-YOLO模型。通过CA注意力机制增强几何特征，SCConv卷积抑制背景噪声和Matrix NMS加速推理优化模型，构建“作物-环境-杂草”耦合图像数据集进行验证。结果表明：杂草识别精度较基线模型提升0.1%~1.3%，推理速度达197.36 FPS。模型精确率、mAP@0.5、召回率分别为92.9%、94.0%、90.7%，均高于主流模型。可视化效果优于主流模型，尤其在杂草小目标检测任务中表现最优，热力图显示对作物与杂草区域具有高度选择性关注。可为大豆玉米间套种植的杂草识别提供高效的技术支持。 |
| 英文摘要: |
| 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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