现代电子技术2025，Vol.48Issue(7)：35-42,8.DOI:10.16652/j.issn.1004-373x.2025.07.006
基于RT-BiSeNet的苹果叶片病害实时分割与分级算法
Apple leaf disease real-time segmentation and grading algorithm based on RT-BiSeNet
摘要
Abstract
Timely segmentation and accurate grading of apple leaf diseases are crucial for improving apple yield and quality.However,in complex environments,images are often affected by factors such as backgrounds with similar colors and varying lighting conditions,which poses significant challenges to the accurate segmentation of leaves and disease spots.These challenges subsequently impact the precision of disease grading.In view of this,a real-time semantic segmentation algorithm,RT-BiSeNet,is proposed for the segmentation and grading of apple leaf diseases.The context path and spatial path of BiSeNet are reconstructed to enhance segmentation accuracy while maintaining real-time processing speed first,and then shallow feature mapping is integrated into the decoder to improve the segmentation effect of leaf edges and small disease spots.The experimental results demonstrate that the mIoU(mean intersection over union)and mPA(mean pixel accuracy)of the RT-BiSeNet algorithm achieve 94.60%and 97.13%,respectively,while reducing the number and complexity of parameters by 85.95%and 72.23%,respectively.The segmentation speed reaches 130.20 f/s,surpassing other real-time segmentation methods.The algorithm can effectively separate leaves and disease spots in real time from complex backgrounds and then grade the diseases according to the established criteria.To sum up,it can provide technical support for accurate prevention and treatment of apple diseases in actual production.关键词
苹果叶片/深度学习/语义分割/BiSeNet/复杂环境/病害分级/实时分割Key words
apple leaf/deep learning/semantic segmentation/BiseNet/complex environment/disease grading/real-time segmentation分类
电子信息工程引用本文复制引用
黄样,陈继清,黄力湘,佘锴蓉,郝科崴..基于RT-BiSeNet的苹果叶片病害实时分割与分级算法[J].现代电子技术,2025,48(7):35-42,8.基金项目
国家自然科学基金项目(62163005) （62163005）
广西自然科学基金项目(2022GXNSFAA035633) （2022GXNSFAA035633）
