农业与技术2026，Vol.46Issue(10)：60-64,5.DOI:10.19754/j.nyyjs.20261030010
基于多模态文本特征融合的YOLOv11水稻病害检测
YOLOv11 Rice Disease Detection Based on Multimodal Text Feature Fusion
摘要
Abstract
To address the limitations of traditional pure visual detection models,including insufficient extraction of similar rice disease features in complex backgrounds and frequent false detection and confusion,this paper explores the introduction of multimodal semantic prior knowledge to improve detection accuracy.This paper proposes a multimodal feature fusion rice disease detection model AgriText-YOLOv11,which combines the LLaVA large language model and YOLOv11.LLaVA generates exclusive natural language description texts for each rice disease image,converts them into embedding vectors,and performs cross-modal network fusion with high-resolution YOLOv11 visual features,enabling the network to focus on key discriminative regions under the guidance of text features.The proposed AgriText-YOLOv11 rice disease detection model increases the parameter quantity by only 5.2％,while improving mAP@0.5 by 1.2 percentage points and recall rate by 2.2 percentage points compared with the baseline model.Its comprehensive performance is superior to mainstream detection models such as YOLOv9,YOLOv10 and RT-DETR,which can realize efficient and accurate localization and classification of rice diseases.关键词
水稻病害/目标检测/多模态融合/大语言模型/YOLOv11Key words
rice diseases/object detection/multimodal fusion/large language models/YOLOv11分类
农业科技引用本文复制引用
肖如君,乔波,彭子淳,黄怡迪..基于多模态文本特征融合的YOLOv11水稻病害检测[J].农业与技术,2026,46(10):60-64,5.基金项目
海南省重点研发项目(项目编号:ZDYF2025GXJS154) （项目编号:ZDYF2025GXJS154）
