中国光学（中英文）2025，Vol.18Issue(1)：160-169,10.DOI:10.37188/CO.2024-0115
激光多普勒测振技术无损检测果冻橙粒化病
Non-destruction detection of jelly orange granulation disease using laser Doppler vibrometry
摘要
Abstract
Granulation is a common internal disease of citrus fruits,and it is difficult to identify the fruits with this disease from their external features.In this study,an acoustic vibration experimental setup was con-structed using a micro-laser Doppler vibrometer(micro-LDV)and a resonance speaker.This was used to col-lect vibration response signals of'Aiyuan 38'jelly orange.The one-dimensional vibration response signals were converted into vibration multi-domain images,and a Resnet-Transformer network(ResT)was construc-ted to extract deeper features from the vibration multi-domain images for identifying granulation disease in jelly oranges.In this paper,the ResT,Resnet50,and Vision Transformer(ViT)models were trained using vi-bration multi-domain images,and their performances were compared.Then,partial least squares discrimin-ant analysis(PLS-DA)and support vector machine(SVM)models were trained using vibration multi-do-main image texture features or vibration spectrum features,and the performance was compared with the ResT model.The results show that the ResT model trained using vibration multi-domain images can achieve accur-ate identification of jelly orange granulation disease with detection accuracy of 98.61％,model F1 of 0.986,precision of 0.986,and recall of 0.986.The proposed method can accurately identify granulated jelly oranges with simplicity,fast speed,and low cost.关键词
激光多普勒测振/声学振动/柑橘粒化病/无损检测/振动多域图像Key words
laser Doppler vibrometry/acoustic vibration/citrus granulation disease/non-destructive detec-tion/vibration multi-domain image分类
电子信息工程引用本文复制引用
刘智,赖庆荣,张天禹,李斌,宋云峰,陈楠..激光多普勒测振技术无损检测果冻橙粒化病[J].中国光学（中英文）,2025,18(1):160-169,10.基金项目
国家重点研发计划(No.2022YFD2001804,No.2023YFD2001301) （No.2022YFD2001804,No.2023YFD2001301）
国家自然科学基金(No.12304447)Supported by National Key Research and Development Program of China(No.2022YFD2001804,No.2023YFD2001301) （No.12304447）
National Natural Science Foundation of China(No.12304447) （No.12304447）
