基于D-S证据理论的苹果霉心病声振检测方法

doi:10.12133/j.smartag.SA202505032

Smart Agriculture ›› 2025, Vol. 7 ›› Issue (4): 119-131.doi: 10.12133/j.smartag.SA202505032

• 专题--农产品品质智能感知与分级 • 上一篇

基于D-S证据理论的苹果霉心病声振检测方法

刘洁¹, 赵康¹^,²^,³, 赵钦君¹, 宋烨²^,³()

^1. 济南大学自动化与电气工程学院，山东济南 250022，中国
^2. 中华全国供销合作总社济南果品研究所，山东济南 250220，中国
^3. 国家果蔬及加工产品质量检验检测中心，山东济南 250220，中国

收稿日期:2025-05-29 出版日期:2025-07-30
基金项目:
山东省重点研发计划项目(2022TZXD007); 济南大学博士启动基金(XBS2494)
作者简介:
刘洁，硕士研究生，研究方向为果蔬无损检测。E-mail：13285479569@163.com
通信作者:
宋烨，博士，研究员，研究方向为果蔬品质智能检测与装备研发。E-mail：songye_sy@163.com

Acoustic-Vibration Detection Method for The Apple Moldy Core Disease Based on D-S Evidence Theory

LIU Jie¹, ZHAO Kang¹^,²^,³, ZHAO Qinjun¹, SONG Ye²^,³()

^1. School of Automation and Electrical Engineering, University of Jinan, Jinan 250022, China
^2. Jinan Fruit Research Institute, All-China Federation of Supply and Marketing Cooperatives, Jinan 250220, China
^3. China Technology and Research Center for Storage and Processing of Fruit and Vegetables, Jinan 250220, China

Received:2025-05-29 Online:2025-07-30
Foundation items:The Key R&D Projects in Shandong Province(2022TZXD007); PhD Start-up Fund of University of Jinan(XBS2494)
About author:
LIU Jie, E-mail: 13285479569@163.com
Corresponding author:
SONG Ye, E-mail: songye_sy@163.com

摘要/Abstract

摘要：

【目的/意义】 霉心病是苹果常见的内部病害，具有较强的传染性。在贮藏早期，霉变症状被限制在果核内部，苹果组织处于亚健康状态，但仍具有商品价值，因此对霉核苹果进行早期检测至关重要。 【方法】 本研究利用声振动无损检测系统采集声振动响应信号，采用对称极坐标变换（Symmetrized Dot Pattern, SDP）、格拉姆角场变换（Gramian Angular Field, GAF）和Stockwell变换（Stockwell Transform, ST），其中GAF分为格拉姆求和场（Gramian Angular Summation Field, GASF）和格拉姆差场（Gramian Angular Difference Field, GADF），以此获得声振多域谱的SDP图、GASF图、GADF图和ST图。利用统一局部二值模式法（Uniform Local Binary Pattern, ULBP）和灰度-梯度共生矩阵法（Gray-Level-Gradient Co-occurrence Matrix, GLGCM）分别从多域可视化图像中提取人工特征，同时设计了基于卷积注意力机制模块和Adam优化器改进的ResNet50特征提取器（Adam-IResNet50）从各域可视化图像中自动提取深度特征，构建最优浅层特征训练分类器多元支持向量机（Multiple Support Vector Machine, MSVM）和最优深层特征训练分类器极限学习机（Extreme Learning Machine, ELM）。将两种分类器初步判别结果转化为独立证据体的基本概率分配，然后基于D-S（Dempster-Shafer, D-S）证据理论中的Dempster合成规则和决策判决规则，获得最终的早期霉心苹果决策判决结果，进而构建声振多域谱浅层特征和深层特征决策层融合模型。 【结果和讨论】 构建的Adam-IResNet50-IPSO-ELM-DS模型对熟知产地3类别苹果多分类的Kappa系数和马修斯相关系数（Matthews Correlation Coefficient, MCC）值略低于90%，F₁和总体准确率（Overall Accuracy, OA）值分别达到了93.01%和93.22%，对亚健康果的判别准确率达到了87.37%，对病害果误判率为8.33%。表明该模型对3类别苹果的分类不仅能较好兼顾查准能力和查全能力，还保持较高检测精度。 【结论】 提出的基于D-S证据理论的苹果霉心病声振检测方法可有效完成检测任务，为今后早期霉心苹果的在线批量化检测提供技术支撑。

关键词: 声学振动技术, 亚健康苹果, 决策层融合, 无损检测

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, transforming the time-domain signals into multi-domain visual spectra to facilitate subsequent feature analysis and recognition. Uniform local 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 applied 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 features 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 features 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 feature extractor. The deep features extracted from the time-domain and frequency-domain GADF images, as well as time-frequency images, 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-vibration 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 accuracy 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 performance improvements. Among them, the Adam-IResNet50-IPSO-ELM-DS model achieved an accuracy of 93.22%, which was significantly higher than that of other methods. This demonstrates that decision-level fusion could effectively enhance the model's discriminative 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 future 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.

Key words: acoustic vibration technology, sub-healthy apple, decision fusion, non-destructive detection

中图分类号:

TP274.5

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.

LIU Jie, ZHAO Kang, ZHAO Qinjun, SONG Ye. Acoustic-Vibration Detection Method for The Apple Moldy Core Disease Based on D-S Evidence Theory[J]. Smart Agriculture, 2025, 7(4): 119-131.

图/表 17

图1

图2

图3

表1

声振多域谱纹理特征参数

序号	特征参数	计算公式	序号	特征参数	计算公式
1	小梯度优势	$G 1 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 H (i, j) / (j + 1) 2 ∑ i = 0 l f - 1 ∑ i = 0 l g - 1 H (i, j)$ （1）	9	梯度均方差	$G 9 = ∑ j = 0 l g - 1 (j - G 7) 2 ∑ i = 0 l f - 1 P (i, j) 12$ （9）
2	大梯度优势	$G 2 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 H (i, j) j 2 ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 H (i, j)$ （2）	10	相关性	$G 10 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 (i - G 6) (j - G 7) P (i, j)$ （10）
3	灰度分布不均匀性	$G 3 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 H (i, j) 2 ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 H (i, j)$ （3）	11	灰度熵	$G 11 = - ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 P (i, j) l o g ∑ j = 0 l g - 1 P (i, j)$ （11）
4	梯度分布不均匀性	$G 4 = ∑ j = 0 l g - 1 ∑ i = 0 l f - 1 H (i, j) 2 ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 H (i, j)$ （4）	12	梯度熵	$G 12 = - ∑ j = 0 l g - 1 ∑ i = 0 l f - 1 P (i, j) l o g ∑ i = 0 l - f 1 P (i, j)$ （12）
5	角二阶距	$G 5 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 [P (i, j)] 2$ （5）	13	混合熵	$G 13 = - ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 P (i, j) l o g P (i, j)$ （13）
6	灰度均值	$G 6 = ∑ i = 0 l f - 1 i ∑ j = 0 l g - 1 P (i, j)$ （6）	14	惯性矩	$G 14 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 (i - j) 2 H ∧ (i, j)$ （14）
7	梯度均值	$G 7 = ∑ j = 0 l g - 1 j ∑ i = 0 l f - 1 P (i, j)$ （7）	15	逆差矩	$G 15 = ∑ i = 0 l f - 1 ∑ j = 0 l g - 1 P (i, j) 1 + (i - j) 2$ （15）
8	灰度均方差	$G 8 = ∑ i = 0 l f - 1 (i - G 6) 2 ∑ j = 0 l g - 1 P (i, j) 12$ （8）

表1

图4

图5

表2

图6

表3

图7

表4

表5

表6

图8

图9

表7

表8

参考文献 28

[1]	LIANG X F, ZHANG R, GLEASON M L, et al. Sustainable apple disease management in China: Challenges and future directions for a transforming industry[J]. Plant disease, 2022, 106(3): 786-799.
[2]	TEMPELAERE A, VAN DOORSELAER L, HE J Q, et al. BraeNet: Internal disorder detection in 'Braeburn' apple using X-ray imaging data[J]. Food control, 2024, 155: ID 110092.
[3]	LI X L, WEI Y Z, XU J, et al. SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology[J]. Postharvest biology and technology, 2018, 143: 112-118.
[4]	GENANGELI A, ALLASIA G, BINDI M, et al. A novel hyperspectral method to detect moldy core in apple fruits[J]. Sensors, 2022, 22(12): ID 4479.
[5]	ZHAO K, ZHA Z H, LI H, et al. Early detection of moldy apple core based on time-frequency images of vibro-acoustic signals[J]. Postharvest biology and technology, 2021, 179: ID 111589.
[6]	ZHANG H, ZHA Z H, KULASIRI D, et al. Detection of early core browning in pears based on statistical features in vibro-acoustic signals[J]. Food and bioprocess technology, 2021, 14(5): 887-897.
[7]	ZHAO K, ZHAO J, YANG Y, et al. Detection of moldy pear core based on the time-frequency analysis of acoustic vibration signals and multi-domain features fusion[J]. Postharvest biology and technology, 2025, 225: ID 113495.
[8]	YIN K, SHEN Y W, CHEN Y F, et al. Multi-source information fusion diagnosis method for aero engine[J]. Applied sciences, 2025, 15(9): ID 5083.
[9]	WANG Y X, LIU F, ZHU A H. Bearing fault diagnosis based on a hybrid classifier ensemble approach and the improved dempster-shafer theory[J]. Sensors, 2019, 19(9): ID 2097.
[10]	蔡浩, 郭宏亮. 基于多分类器DS证据理论融合的水果识别研究[J]. 中国农机化学报, 2021, 42(2): 184-189.
	CAI H, GUO H L. Research on fruit recognition based on multi-classifier DS evidence theory fusion[J]. Journal of Chinese agricultural mechanization, 2021, 42(2): 184-189.
[11]	毕淑慧, 李雪, 申涛, 等. 基于多模型证据融合的苹果分类方法[J]. 农业工程学报, 2022, 38(13): 141-149.
	BI S H, LI X, SHEN T, et al. Apple classification based on evidence theory and multiple models[J]. Transactions of the Chinese society of agricultural engineering, 2022, 38(13): 141-149.
[12]	YAN X W, MA L Y, BI S H, et al.Application of DS evidence theory in apple's internal quality classification[C]// The 10th International Conference on Computer Engineering and Networks. Singapore: Springer Singapore, 2020: 563-571.
[13]	LI L, PENG Y K, LI Y Y, et al. Rapid and low-cost detection of moldy apple core based on an optical sensor system[J]. Postharvest biology and technology, 2020, 168: ID 111276.
[14]	ZHANG H, JI S, WANG K, et al. Detection of early browning in pears based on time-frequency images of vibro-acoustic signals and improved MobileNetV3[J]. Food and bioprocess technology, 2025, 18(3): 2721-2736.
[15]	GUO Z M, WANG M M, AGYEKUM A A, et al. Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy[J]. Journal of food engineering, 2020, 279: ID 109955.
[16]	KHALEEFAH S H, MOSTAFA S A, MUSTAPHA A, et al. The ideal effect of Gabor filters and Uniform Local Binary Pattern combinations on deformed scanned paper images[J]. Journal of king Saud university - computer and information sciences, 2021, 33(10): 1219-1230.
[17]	TU B, KUANG W L, ZHAO G Z, et al.Hyperspectral image classification by combining local binary pattern and joint sparse representation[J]. International journal of remote sensing, 2019, 40(24): 9484-9500.
[18]	LAKSHMI SOWJANYA U, KRITHIGA R. Decoding student emotions: An advanced CNN approach for behavior analysis application using uniform local binary pattern[J]. IEEE access, 2024, 12: 106273-106284.
[19]	PAN Z B, HU S Q, WU X Q, et al. Adaptive center pixel selection strategy in Local Binary Pattern for texture classification[J]. Expert systems with applications, 2021, 180: ID 115123.
[20]	KHAN K A, SHANIR P P, KHAN Y U, et al. A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy[J]. Expert systems with applications, 2020, 140: ID 112895.
[21]	ZHAO X, LI K Y, LI Y X, et al. Identification method of vegetable diseases based on transfer learning and attention mechanism[J]. Computers and electronics in agriculture, 2022, 193: ID 106703.
[22]	WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional Block attention module[C]// Computer Vision-ECCV 2018. Cham, Germany: Springer, 2018: 3-19.
[23]	赵康, 查志华, 李贺, 等. 基于声振信号对称极坐标图像的苹果霉心病早期检测[J]. 农业工程学报, 2021, 37(18): 290-298.
	ZHAO K, ZHA Z H, LI H, et al. Early detection of moldy apple core using symmetrized dot pattern images of vibro-acoustic signals[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37(18): 290-298.
[24]	ODHIAMBO OMUYA E, ONYANGO OKEYO G, WAEMA KIMWELE M. Feature selection for classification using principal component analysis and information gain[J]. Expert systems with applications, 2021, 174: ID 114765.
[25]	SHAO R P, HU W T, WANG Y Y, et al. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform[J]. Measurement, 2014, 54: 118-132.
[26]	张帅. 基于优化型混合核函数支持向量机的个人信用评估[D]. 太原: 太原理工大学, 2020.
	ZHANG S.Personal credit evaluation based on optimized mixed kernel function support vector machine[D]. Taiyuan: Taiyuan University of Technology, 2020.
[27]	KHAN M, HOODA B K, GAUR A, et al. Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes[J]. Scientific reports, 2024, 14(1): ID 22728.
[28]	LIU G Y, HAN X, TIAN L, et al. ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features[J]. Computer methods and programs in biomedicine, 2021, 208: ID 106269.

数据集类型		筛选的敏感特征参数	数据集类型		筛选的敏感特征参数
时域信号统计特征		TD ₂、TD ₇、TD ₁₈、TD ₅ 、 TD ₉、TD ₁₁、TD ₁₀、TD ₆	频域信号统计特征		FD ₇、FD ₂、FD ₃、FD ₈
时域 SDP图	GLGCM	TD _G9、TD _G13、TD _G6 、 TD _G15、TD _G10、TD _G7	频域GASF图	GLGCM	FD _G6、FD _G9、FD _G13 、 FD _G1、FD _G11、FD _G14
时域 SDP图	ULBP	TD _U6、TD _U28、TD _U13、TD _U9、TD _U30、TD _U52、TD _U24、TD _U16、TD _U12、TD_U ₃₆、TD _U7、TD _U46、TD _U42、TD _U25、TD _U10	频域GASF图	ULBP	FD _U10、FD_U ₃₃、FD _U20、FD _U49、FD _U35、FD _U18、FD _U24、FD _U28、FD _U45、FD _U11、FD _U54、FD _U32、FD _U6、FD _U15 、FD _U39、FD _U13、FD _U2
时域GASF图	GLGCM	TD _G8、TD _G14、TD _G11 、 TD _G9、TD _G10	频域GADF图	GLGCM	FD _G6、FD _G9、FD _G13 、 FD _G11、FD _G14、FD _G1
时域GASF图	ULBP	TD _U12、TD _U3、TD _U21、TD _U6、TD _U35、TD _U22、TD _U15、TD _U38、TD _U26、TD _U10、TD _U42、TD _U14、TD _U33、TD _U50	频域GADF图	ULBP	FD _U16、FD _U25、FD _U8、FD _U32、FD _U20、FD _U7、FD _U43、FD _U50、FD _U21、FD _U5、FD _U12、FD _U38、FD _U23、FD _U44、FD _U15、FD _U31
时域GADF图	GLGCM	TD _G8、TD _G14、TD _G9 、 TD _G1、TD _G13	时频域ST图	GLGCM	TFD _G10、TFD _G13、TFD _G5、TFD _G15、TFD _G6、TFD _G4、TFD _G14
时域GADF图	ULBP	TD _U3、TD _U20、TD _U15、TD_U ₂₃、TD _U8、TD _U27、TD _U53、TD _U19、TD _U24、TD _U45、TD _U7、TD _U13、TD _U36、TD _U42	时频域ST图	ULBP	TFD _U9、TFD _U23、TFD _U16、TFD _U32、TFD _U14、TFD _U43、TFD _U38、TFD _U11、TFD _U25、TFD _U58、TFD _U12、TFD _U30、TFD _U44、TFD _U52、TFD _U27、TFD _U8、TFD _U2、TFD _U15、TFD _U46、TFD _U6

特征		分类准确率/%
时域特征	统计特征	75.75
	TD-SDP-GLGCM	79.42
	TD-SDP-ULBP-PC	76.83
	TD-GASF-GLGCM	80.03
	TD-GASF-ULBP-PC	78.34
	TD-GADF-GLGCM	81.62
	TD-GADF-ULBP-PC	85.20
频域特征	统计特征	78.31
	FD-GASF-GLGCM	75.27
	FD-GASF-ULBP-PC	80.59
	FD-GADF-GLGCM	83.20
	FD-GADF-ULBP-PC	88.94
时频域特征	TFD-ST-GLGCM	93.53
时频域特征	TFD-ST-ULBP-PC	91.62

分类器	各单一域深层特征（V_i ）	总体分类准确率（A_i ）/%
ELM	TD-GADF	89.05
	FD-GADF	91.27
	TFD-ST	95.49

特征类型	特征名称	分类器	证据体	各证据体的基本概率分配值m_i （L_j ）
特征类型	特征名称	分类器	证据体	L ₁	L ₂	L ₃	Θ
浅层特征	TD-GADF-ULBP-PC	MSVM	E ₁₁	0.295	0.273	0.306	0.126
	FD-GADF-ULBP-PC		E ₁₂	0.300	0.292	0.323	0.085
	TFD-ST-GLGCM		E ₁₃	0.312	0.306	0.351	0.031
深层特征	TD-GADF	ELM	E ₂₁	0.298	0.286	0.314	0.102
	FD-GADF		E ₂₂	0.312	0.294	0.366	0.028
	TFD-ST		E ₂₃	0.319	0.298	0.382	0.001

分类模型	模型参数							分类准确率/%
分类模型	α	c	σ	d	l	ω_j	b_j	分类准确率/%
IPSO-MSVM-DS	0.638	68.325	9.651	2	—	—	—	98.52
Adam-IResNet50-IPSO-ELM-DS	—	—	—	—	10	2.542	0.121	99.36

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

在线阅读

知网下载

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 28

相关文章 4

编辑推荐

Metrics

本文评价

分类模型	总体分类准确率/%	分类模型	总体分类准确率/%
TD-GADF-ULBP-PC-MSVM	71.64	FD-GADF-IResNet50-ELM	80.37
FD-GADF-ULBP-PC-MSVM	77.52	TFD-ST-IResNet50-ELM	85.82
TFD-ST-GLGCM-MSVM	82.16	IPSO-MSVM-DS	87.45
TD-GADF-IResNet50-ELM	75.33	Adam-IResNet50-IPSO-ELM-DS	93.22

产地	实际类别	预测类别			Kappa/%	MCC/%	F ₁/%	OA/%
产地	实际类别	健康	亚健康	病害	Kappa/%	MCC/%	F ₁/%	OA/%
熟知产地	健康	106	2	0	89.66	89.68	93.01	93.22
	亚健康	5	83	7
	病害	0	6	86
陌生产地	健康	68	4	0	85.41	85.55	90.21	90.36
	亚健康	4	55	6
	病害	0	5	55

[1]	贾文珅, 吕浩林, 张上, 秦英栋, 周巍. 利用便捷式可见-近红外光谱仪和机器学习分辨霉变小麦及霉变程度[J]. 智慧农业(中英文), 2024, 6(1): 89-100.
[2]	李阳, 彭彦昆, 吕德才, 李永玉, 刘乐, 朱宇杰. 可移动式苹果内部品质果园产地分级系统[J]. 智慧农业(中英文), 2022, 4(3): 132-142.
[3]	郭志明, 王郡艺, 宋烨, 邹小波, 蔡健荣. 果蔬品质劣变传感检测与监测技术研究进展[J]. 智慧农业(中英文), 2021, 3(4): 14-28.
[4]	曹玉栋, 祁伟彦, 李娴, 李哲敏. 苹果无损检测和品质分级技术研究进展及展望[J]. 智慧农业(中英文), 2019, 1(3): 29-45.