山东农业大学学报（自然科学版）2026，Vol.57Issue(3)：569-582,14.DOI:10.3969/j.issn.1000-2324.2026.03.017
基于特征-边界协同建模的茶叶病虫害识别方法
A Tea Pest and Disease Identification Method Based on Feature-Boundary Collaborative Modeling
摘要
Abstract
Due to the high similarity and small target characteristics of tea pests and diseases,issues such as missed detection,false detection,and inaccurate positioning are prone to occur in intelligent recognition tasks under complex scenes.Based on the YOLOv12 network framework,this paper proposes a feature-boundary collaborative modeling method for tea pest and disease identification.Firstly,it designs a parallel hierarchical densely-connected residual mixing mechanism to enhance the detailed expression capacity of images in different receptive fields,thereby improving the model's recognition accuracy for blurred boundaries and complex-textured lesions.Then,based on the feature-boundary collaborative modeling,it designs a frequency-domain adaptive modulation strategy to enhance the model's perceptual response capacity to high-frequency lesion features.Finally,it constructs a boundary-integral fuzzy Dice loss function,effectively strengthening the model's structural constraints and geometric fitting capability for lesion target contours.The experimental results show that the proposed improved model exhibits outstanding performance in tea pest and disease identification tasks.The recognition precision,recall,and mAP reach 94.9%,98.6%,and 96.8%,respectively,representing improvements of 3.6%,3.5%,and 2.7%compared to the original YOLOv12.Moreover,the model maintains a high level of real-time performance,with an inference speed of 45.7 f/s.This provides a reliable technical pathway for intelligent identification of tea pests and diseases in precision agriculture.关键词
茶叶病虫害识别/YOLOv12/频域自适应调制/特征-边界协同建模/并行分层密联残差混合/边界积分模糊Dice损失Key words
Tea pest and disease identification/YOLOv12/frequency-domain adaptive modulation/feature-boundary collaborative modeling/parallel hierarchical densely-connected residual mixing/boundary-integral fuzzy dice loss分类
信息技术与安全科学引用本文复制引用
王晓婷,赵展..基于特征-边界协同建模的茶叶病虫害识别方法[J].山东农业大学学报（自然科学版）,2026,57(3):569-582,14.基金项目
河南省高等学校重点科研项目计划(24B520025) （24B520025）
开封市科技发展计划项目(2402002) （2402002）
