基于声振信号经验模态分解的香梨黑心病早期无损检测  

Nondestructive Detection of Pear with Early-stage Core Browning Based on Empirical Mode Decomposition of Vibro-acoustic Signals

作　　者：李贺 赵康 查志华 吴杰[1,2,3] LI He;ZHAO Kang;ZHA Zhihua;WU Jie(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832003,China;Key Laboratory of Northwest Agricultural Equipment,Ministry of Agriculture and Rural Affairs,Shihezi 832003,China;Research Center of Agricultural Mechanization for Economic Crop in Oasis,Ministry of Education,Shihezi 832003,China)

机构地区：[1]石河子大学机械电气工程学院,新疆石河子832003 [2]农业农村部西北农业装备重点实验室,新疆石河子832003 [3]绿洲特色经济作物生产机械化教育部工程研究中心,新疆石河子832003

出　　处：《食品科学》2023年第20期357-371,共15页Food Science

基　　金：国家自然科学基金地区科学基金项目(31560476)。

摘　　要：首先通过声振无损检测系统获取香梨响应信号,对信号进行经验模态分解并采用不同方法抑制其端点效应和模态混叠以实现信号分解效果最优;然后将香梨声振信号分解的分量作为输入,构建基于空间金字塔池化的卷积神经网络(convolutional neural networks with space pyramid pooling,CNN-SPP)判别模型。结果表明,改进斜率法能更有效抑制香梨声振信号分解端点效应,进一步采用互补自适应噪声完备集合经验模态分解(complementary complete ensemble empirical mode decomposition with adaptive noise,CCEEMDAN)方法可获得模态混叠抑制最佳的信号分量,以此为输入构建CCEEMDAN-CNN-SPP判别模型对香梨黑心病的总体分类准确率为93.66%,对亚健康香梨判别准确率达94.44%,病害果误判率为6.35%,提高了声振法对梨果早期轻度病害的判别精度,为声振法应用于亚健康水果在线检测系统研发提供了研究基础。In this study,a nondestructive vibro-acoustic setup was employed to acquire the vibro-acoustic signals of pear fruit.The signals were decomposed using the empirical mode decomposition(EMD).Different methods were used to suppress the end effect and mode mixing to achieve the optimal signal decomposition components.Then,the decomposition components of the vibro-acoustic signals were used as the input to construct a discriminant model based on convolution neural networks with spatial pyramid pool(CNN-SPP).The results showed that the improved slope-based method was better able to suppress the EMD end effect for the vibro-acoustic signals.The complementary complete ensemble empirical mode decomposition with adaptive noise(CCEEMDAN)method could exhibit better performance for suppressing mode mixing after end effect suppression.Thus,the obtained components were used as the input to construct a CCEEMDAN-CNN-SPPbased discriminant model.The overall classification accuracy of the model was 93.66%for pears with core browning,the discrimination accuracy was 94.44%for sub-healthy pears,and the misjudgment rate was 6.35%for diseased fruit.This improved the accuracy of vibro-acoustic identification of pears with early-stage mild disease.This study lays a foundation for the development of an online detection system for sub-healthy fruits in the future.

关 键 词：香梨黑心病 无损检测 声振法 经验模态分解 卷积神经网络 

分 类 号：S661.2[农业科学—果树学] TP391.4[农业科学—园艺学]