智慧农业（中英文）2025，Vol.7Issue(4)：119-131,13.DOI:10.12133/j.smartag.SA202505032
基于D-S证据理论的苹果霉心病声振检测方法
Acoustic-Vibration Detection Method for The Apple Moldy Core Disease Based on D-S Evidence Theory
摘要
Abstract
[Objective]Moldy core disease is a common internal disease of the apple and is highly infectious.In the early storage stage,the mold symptoms are confined to the interior of the core.The apple tissue is in a sub-healthy state and still has commercial value,so early detection of moldy-core apples is critical.[Methods]In this study,a non-destructive acoustic vibration detection system was used to acquire acoustic vibration response signals.Symmetrized dot pattern(SDP),gramian angular field(GAF),and stockwell transform(ST)were applied to obtain multi-domain acoustic vibration spectra,including SDP images,gramian angular summation field(GASF)images,gramian angular difference field(GADF)images,and ST images.These images were uniformly converted to grayscale,trans-forming the time-domain signals into multi-domain visual spectra to facilitate subsequent feature analysis and recognition.Uniform lo-cal binary pattern(ULBP)and gray-level-gradient co-occurrence matrix(GLGCM)methods were used to extract handcrafted features from the multi-domain visualized images.Subsequently,the maximum relevance and minimum redundancy(mRMR)criterion was ap-plied to select the dominant features from each analysis domain that were sensitive to early disease information.Principal component analysis(PCA)was employed to reduce the dimensionality of the multi-domain spectral ULBP texture features.From the statistical features extracted from one-dimensional acoustic-vibration signals in the time and frequency domains,and the GLGCM texture fea-tures extracted from two-dimensional images in each domain,5 to 8 features sensitive to early moldy core detection were selected.From the high-dimensional sensitive ULBP texture features extracted from each domain,3 to 7 principal components were obtained through dimensionality reduction using principal component analysis.This selection aimed to maximize the relevance between fea-tures and class labels while minimizing redundancy among features,thereby identifying the most informative features for early mold core apple detection in each domain.Meanwhile,a ResNet50 feature extractor improved with a convolutional attention mechanism module and the Adam optimizer(Adam-IResNet50)was designed to automatically extract deep features from visualized images in each domain.The optimal shallow features were used to train a multiple support vector machine(MSVM)classifier,while the optimal deep features were used to train an extreme learning machine(ELM)classifier.The Adam-IResNet50 network was employed as a fea-ture extractor.The deep features extracted from the time-domain and frequency-domain GADF images,as well as time-frequency im-ages,resulted in higher sample matching scores(SC)and cluster compactness(CHS)values,along with lower inter-class overlap(DBI)values for the three apple categories.These results clearly indicate that the deep features extracted by the Adam-IResNet50 model from multi-domain images exhibit strong capability in identifying subhealth and moldy core apples.The preliminary outputs of the two classifiers were converted into basic probability assignments for independent evidence bodies.Dempster's combination rule and the associated decision criterion of Dempster-Shafer(D-S)theory were then applied to yield the final decision on early-stage moldy apples.Consequently,a decision-level fusion model was established for both shallow and deep features of the acoustic-vibra-tion multi-domain spectra.[Results and Discussions]The constructed Adam-IResNet50-IPSO-ELM-DS model based on D-S evidence theory achieved a Kappa coefficient and Matthews Correlation Coefficient(MCC)slightly below 90%for multi-class classification of apples from known origins.The F1-Score and Overall Accuracy(OA)reached 93.01%and 93.22%,respectively.The classification ac-curacy for sub-healthy apples was 87.37%,while the misclassification rate for diseased apples was 8.33%.These results indicate that the model maintains a balanced precision and recall while achieving high detection accuracy for three classes of apples from unknown origins.After decision fusion,the IPSO-MSVM-DS and Adam-IResNet50-IPSO-ELM-DS models demonstrated significant perfor-mance improvements.Among them,the Adam-IResNet50-IPSO-ELM-DS model achieved an accuracy of 93.22%,which was signifi-cantly higher than that of other methods.This demonstrates that decision-level fusion could effectively enhance the model's discrimi-native ability and further improve classification performance.[Conclusions]The proposed acoustic vibration detection method for mold core apples,based on Dempster-Shafer evidence theory,provides technical support for future online batch detection of early mold core apples.Early screening of sub-healthy apples is of great significance for quality control during postharvest storage.In fu-ture work,the model will be further optimized to develop a rapid acoustic vibration-based prediction method for early detection of mold core,providing technical support for quality control during apple distribution.关键词
声学振动技术/亚健康苹果/决策层融合/无损检测Key words
acoustic vibration technology/sub-healthy apple/decision fusion/non-destructive detection分类
信息技术与安全科学引用本文复制引用
刘洁,赵康,赵钦君,宋烨..基于D-S证据理论的苹果霉心病声振检测方法[J].智慧农业（中英文）,2025,7(4):119-131,13.基金项目
山东省重点研发计划项目(2022TZXD007) （2022TZXD007）
济南大学博士启动基金(XBS2494) The Key R&D Projects in Shandong Province(2022TZXD007) （XBS2494）
PhD Start-up Fund of University of Jinan(XBS2494) （XBS2494）
