农业机械学报2026，Vol.57Issue(18)：51-61,11.DOI:10.6041/j.issn.1000-1298.2026.18.005
融合语义与图像感知的棉花病虫害检测方法
Cotton Pests and Diseases Detection Method Based on Integration of Semantic and Image Perception
摘要
Abstract
Aiming to address the bottlenecks posed by the significant variation in the target scales of cotton field pests and diseases and the limited generalization capability of single-modal visual representations,a multimodal detection method that integrated semantic prior knowledge with multi-scale image perception was proposed.The method used a frozen CLIP model as the foundation for feature extraction.By leveraging a text adaptation module and a parameter-efficient fine-tuning network,it converted agronomic text into structured prompts,thereby achieving deep alignment between agronomic semantics and visual modalities.Additionally,a prototype-guided attention mechanism was designed to dynamically link pixel distributions with the semantic space based on learnable prototypes.At the detection model,VGG19 served as the backbone network,integrating the global context modeling capabilities of swin transformer.Additionally,SFM and IFM modules were introduced to construct a continuous-scale feature flow,thereby mitigating the truncation of fine-scale object features caused by traditional discrete downsampling.Experiments demonstrated that the proposed model achieved an mAP of 92.5％on the cotton pest and disease dataset,outperforming other mainstream models YOLO v5s and YOLO v8n by 3.6 and 4.6 percentage points,respectively.Ablation results further revealed that the semantic guidance and multi-scale optimization strategies exhibited a significant synergistic effect in performance improvement,while the model's parameter count was only 1.56×106,which was much lower than that of YOLO v5s and YOLO v8n.Additionally,Grad-CAM visualization results indicated that the model can consistently focus on typical lesion areas.In summary,this method achieved a balance between accuracy and model compactness,providing a reference for the precise identification of pests and diseases under complex agricultural conditions and for edge deployment.关键词
棉花/病虫害/多模态融合/语义先验/注意力机制Key words
cotton/pests and diseases/multimodal fusion/semantic prior/attention mechanism分类
农业科技引用本文复制引用
李阳,吴科,聂晶,方凯..融合语义与图像感知的棉花病虫害检测方法[J].农业机械学报,2026,57(18):51-61,11.基金项目
石河子大学青年创新拔尖人才计划项目(CXBJ202306)和浙江省尖兵领雁+X科技项目(2026C04008) （CXBJ202306）
