湖北农业科学2026，Vol.65Issue(7)：194-200,207,8.DOI:10.14088/j.cnki.issn0439-8114.2026.07.030
基于多尺度反馈聚合增强的YOLOv12植物病害检测框架
YOLOv12-based framework for plant disease detection enhanced by multi-scale feedback aggregation
摘要
Abstract
To address the problems of difficult small-object recognition,insufficient feature fusion,and complex background interfer-ence in plant disease detection,a multi-scale feedback aggregation detection model based on improved YOLOv12 was proposed.The model was validated on the PlantDoc dataset containing images of 30 categories of plant leaf diseases to improve the detection accuracy and robustness of lesion regions in complex environments.By introducing the Multi-Scale Enhanced Multi-Branch Aggregation with Feedback(MEMBA-F)module into the YOLOv12 framework,the improved model employed multi-branch convolution and a bidirec-tional information feedback mechanism to achieve adaptive cross-layer feature fusion,thereby enhancing the representation of lesion edges and fine-grained features.The results showed that the Precision,Recall,mAP50,and mAP50-95 of the improved model on the PlantDoc dataset reached 0.573,0.372,0.582,and 0.365,respectively,which were 7.7％,1.4％,3.7％,and 7.7％higher than those of YOLOv8,while maintaining an inference speed of 27.4 ms,demonstrating real-time detection capability.Compared with the baseline YOLOv12 model,the metrics of the improved model were further enhanced,verifying the effectiveness of the MEMBA-F module in multi-scale feature aggregation,cross-layer information interaction,and the representation of small objects and fine-grained features.The improved model balanced detection accuracy and efficiency,providing a reference for plant disease recognition in smart agriculture.关键词
植物病害检测/YOLOv12/多尺度特征融合/MEMBA-F模块Key words
plant disease detection/YOLOv12/multi-scale feature fusion/MEMBA-F module分类
信息技术与安全科学引用本文复制引用
陈凌,甘露,陈嘉颖,陈剑峰,戴曲顺..基于多尺度反馈聚合增强的YOLOv12植物病害检测框架[J].湖北农业科学,2026,65(7):194-200,207,8.基金项目
南平市自然科学基金联合项目(N2025J014) （N2025J014）
福建省中青年教师教育科研项目(JZ230078) （JZ230078）
福建林业职业技术学院高层次人才课题(2023BK13) （2023BK13）
福建林业职业技术学院教研课题(YBJY202513) （YBJY202513）
