基于特征-边界协同建模的茶叶病虫害识别方法OA

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.