融合语义与图像感知的棉花病虫害检测方法OA

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.