Recently， intelligent management of crop diseases has made considerable progress. However， cotton leaf disease still poses significant challenges due to small target sizes and blurred leaf boundaries， and there is a lack of models that integrate “recognition-diagnosis-decision-making.” Therefore， an intelligent diagnosis model for cotton leaf diseases was proposed. First， a dataset containing 1 320 entries of cotton cultivation question and answer was constructed to provide data basis for subsequent fine-tuning of the Tongyi Qianwen large language model. Second， the Multi-Scale Simple Attention Module （MSSimAM） was introduced at the end of the YOLOv12n backbone to highlight multi-scale disease-related features and suppress redundant information. Third， in the detection head， the Adaptive Threshold Focal Loss （ATFL） function was employed to address the imbalance between easy and hard samples， thereby forming the MN-YOLO model. Finally， MN-YOLO model was integrated with the fine-tuned Tongyi Qianwen large language model to generate disease prevention and treatment measure recommendations based on the detection results. Experimental results indicates that MN-YOLO model achieves a mean Average Precision at Intersection over Union （IoU） threshold of 0.5 （mAP₅₀） of 96.9% while maintaining a low parameter count， representing increases of 2.0 percentage points in mAP₅₀， 0.5 percentage points in precision， and 4.5 percentage points in recall compared with YOLOv12n. In generalization experiments， the proposed model also demonstrates strong adaptability and stability across different datasets. The results indicate that this diagnosis model for cotton leaf diseases combines disease detection and treatment measure recommendation， improving detection precision while providing effective support for smart agriculture.