智慧农业（中英文）2025，Vol.7Issue(4)：31-46,16.DOI:10.12133/j.smartag.SA202505036
多源数据融合技术在苹果无损检测中的应用研究进展
Advances in the Application of Multi-source Data Fusion Technology in Non-Destructive Detection of Apple
摘要
Abstract
[Significance]Apple industry is a prominent agricultural sector that is of considerable importance globally.Ensuring the high-est standards of quality and safety is paramount for achieving consumer satisfaction.Non-destructive testing technologies have emerged as a powerful tool,enabling rapid and objective evaluation of fruit attributes.However,individual non-destructive testing technologies methods frequently possess inherent limitations,proving insufficient for comprehensive assessment.The synergistic ap-plication of multi-source data fusion technology in the non-destructive testing integrates information from multiple sensors to over-come the shortcomings of single-modality systems.The integration of disparate data streams constitutes the foundational technologi-cal framework that enables the advancement of apple quality control.This technological framework facilitates enhanced detection of defects and diseases,thereby contributing to the intelligent transformation of the apple industry value chain in its entirety.[Progress]This paper presents a systematic and comprehensive examination of recent advancements in multi-source data fusion for apple non-de-structive testing.The principles,advantages,and typical application scenarios of five mainstream non-destructive testing technologies are first introduced:near-infrared(NIR)spectroscopy,particularly adept at quantifying internal chemical compositions such as soluble solids content(SSC)and firmness by analyzing molecular vibrations;hyperspectral imaging(HSI),which combines spectroscopy and imaging to provide both spatial and spectral information,making it ideal for visualizing the distribution of chemical components and identifying defects like bruises;electronic nose(E-nose)technology,a method for detecting unique patterns of volatile organic com-pounds(VOCs)to profile aroma and detect mold;machine vision,a process that analyzes external features such as color,size,shape,and texture for grading and surface defect identification;and nuclear magnetic resonance(NMR),a technique that provides detailed in-sights into internal structures and water content,useful for detecting internal defects such as core rot.A critical evaluation of the funda-mental methodologies in data fusion is conducted,with these methodologies categorized into three distinct levels.Data-level fusion entails the direct concatenation of raw data from homogeneous sensors or preprocessed heterogeneous sensors.This approach is straightforward.It can result in high dimensionality and is susceptible to issues related to data co-registration.Feature-level fusion,the most prevalent strategy,involves extracting salient features from each data source(e.g.,spectral wavelengths,textural features,gas sensor responses)and subsequently combining these feature vectors prior to model training.This intermediate approach effectively re-duces redundancy and noise,and enhances model robustness.Decision-level fusion operates at the highest level of abstraction,where independent models are trained for each data modality,and their outputs or predictions are integrated using algorithms such as weight-ed averaging,voting schemes,or fuzzy logic.This strategy offers maximum flexibility for integrating highly disparate data types.The paper also thoroughly elaborates on the practical implementation of these strategies,and presents case studies on the fusion of differ-ent spectral data(e.g.,NIR and HSI),the integration of spectral and E-nose data for combined internal quality and aroma assessment,and the powerful combination of machine vision with spectral data for simultaneous evaluation of external appearance and internal composition.[Conclusions and Prospects]The integration of multi-source data fusion technology has driven significant advancements in the field of apple non-destructive testing.This progress has substantially improved the accuracy,reliability,and comprehensiveness of quality evaluation and control systems.By synergistically combining the strengths of different sensors,it enables a holistic assess-ment that is unattainable with any single technology.However,the field faces persistent challenges,including the effective manage-ment of data heterogeneity(i.e.,varying scales,dimensions,and physical meanings),the high computational complexity of sophisticat-ed fusion models,and the poor portability of current multi-sensor laboratory equipment—all of which hinder online industrial applica-tions.Future research should prioritize several key areas.First,developing automated,user-friendly fusion platforms is imperative to simplify data processing and model deployment.Second,optimizing and developing lightweight algorithms(e.g.,through model com-pression and knowledge distillation)is critical to enhancing real-time performance for high-throughput sorting lines.Third,creating compact,cost-effective,integrated hardware that combines multiple detection technologies into a single portable device will improve stability and accessibility.Additionally,new application frontiers should be explored,such as in-field monitoring of fruit maturation and predicting post-harvest shelf life.The innovative integration of advanced algorithms and hardware holds the potential to provide substantial support for the intelligent and sustainable development of the global apple industry.关键词
多源数据融合/苹果/无损检测/品质评估/病害诊断/光谱/电子鼻/机器视觉Key words
multi-source data fusion/apple/non-destructive detection/quality evaluation/disease diagnosis/spectrum/electronic nose/machine vision分类
信息技术与安全科学引用本文复制引用
郭琪,宋烨,范艺璇,闫新焕,刘雪梅,曹宁,王震,潘少香,谭梦男,郑晓冬..多源数据融合技术在苹果无损检测中的应用研究进展[J].智慧农业（中英文）,2025,7(4):31-46,16.基金项目
山东省重点研发计划项目(2024TZXD063) （2024TZXD063）
中华全国供销合作总社科技创新项目(GXKJ-2024-016) Shandong Province Key R&D Programme(2024TZXD063) （GXKJ-2024-016）
All China Federation of Supply and Marketing Cooper-atives Science and Technology Innovation Project(GXKJ-2024-016) （GXKJ-2024-016）
