农业机械学报2026，Vol.57Issue(18)：106-116,11.DOI:10.6041/j.issn.1000-1298.2026.18.010
面向无人机巡检的玉米病虫害图文协同检测模型
Image-Text Collaborative Detection Model for Corn Pests and Diseases in Drone-based Inspections
摘要
Abstract
Combining drone inspection with deep learning for maize pest and disease detection represents a trend in the development of smart agriculture.However,the detection performance of deep learning models is influenced not only by the number of image samples but also constrained by factors such as field environment,imaging quality,and model generalization capability,making it difficult for a single image modality to fully characterize pest and disease features.To address the above issues,a vision-language collaborative detection model for maize pests and diseases under drone inspection was proposed based on the YOLO-World framework,integrating textual semantic information.The model reconstructed the image feature extraction network by incorporating a re-parameterized visual geometry network,attention mechanisms,and multi-scale fusion modules,thereby improving the multi-scale image feature extraction capability.By adding a feature linear modulation layer and multi-class attention to the text-injected image,and introducing a feed forward module,gating mechanism,and residual structure to the image-embedded text,the re-parameterizable vision-language path aggregation network was enhanced,which improved the semantic alignment capability and expression accuracy between image and text features.In the experiments,totally 500 corn pest and disease images were captured from a drone inspection perspective,including two categories:early corn yellow leaf disease and leaf rot caused by pests.The dataset was augmented to 2 000 images,and a multi-modal dataset in COCO format was constructed by incorporating corresponding textual descriptions.Experimental results showed that on the 500 image dataset(with 25-shot and 50-shot training samples respectively and a fixed validation set of 400 images),the proposed model improved mAP@0.5 by 9.40～10.40 percentage points over the single modal YOLO v8 series and by 2.80～4.40 percentage points over YOLO-World.On the 2 000 image dataset(with an8∶2 training-validation split),mAP@0.5 was improved by 5.40～10.70 percentage points over the single modal YOLO v8 series and by 3.10 percentage points over YOLO-World.This model can provide a research approach for identifying corn pests and diseases under few-shot conditions in drone based inspection.关键词
玉米病虫害/无人机巡检/YOLO-World/多模态/小样本检测Key words
corn diseases and pests/unmanned aerial vehicle inspection/YOLO-World/multi-modal/few-shot object detection引用本文复制引用
承达瑜,苏皓,梁优,赵伟,王建东,宋辞..面向无人机巡检的玉米病虫害图文协同检测模型[J].农业机械学报,2026,57(18):106-116,11.基金项目
河北省重大科技成果转化专项(22287401Z)、国家自然科学基金项目(42171212)和地理信息科学与技术全国重点实验室开放基金项目 （22287401Z）
