蔬菜Issue(8)：58-80,23.DOI:10.26924/j.cnki.sczz.2026.8005
多源数据驱动的蔬菜品质无损检测研究进展
Advances in Multi-source Data-Driven Nondestructive Assessment of Vegetable Quality
摘要
Abstract
Nondestructive assessment of vegetable quality is a priority research area in smart agriculture and food safety.Conventional analytical methods are inefficient and destructive,and thus fall short of modern demands for rapid and precise quality control.In recent years,multisource,data-driven nondestructive technologies,integrating imaging,spectroscopy,acoustics,vibration,and biomimetic sensory modalities,have enabled comprehensive characterization of multidimensional quality attributes,including appearance,internal composition,defects,and flavor,markedly improving accuracy and intelligence.This review systematically surveys advances in visible imaging,hyperspectral imaging,near-infrared spectroscopy,Raman spectroscopy and fluorescence spectroscopy,acoustic-vibration sensing,and bionic sensory data for vegetable quality assessment.It analyzes the strengths and limitations of single-source data and discusses the potential and challenges of multisource data fusion for enhancing model robustness.Despite notable laboratory-level progress,persistent issues remain,including limited model generalization,high instrumentation costs,and the algorithmic complexity of data fusion.Future work should prioritize cross-modal feature fusion,construction of large-scale databases,integration of lightweight sensors,and development of end-to-end quality traceability systems to enable widespread adoption of multisource nondestructive technologies in real-world agricultural production.关键词
蔬菜品质/无损检测/图像数据/光谱数据/仿生感官数据/多源数据融合Key words
vegetable quality/nondestructive assessment/imaging data/spectral data/biomimetic sensory data/multisource data fusion分类
农业科技引用本文复制引用
庄绿涵,郝浩源,奚嘉艺,王作辉,樊瑶瑶,黄文倩..多源数据驱动的蔬菜品质无损检测研究进展[J].蔬菜,2026,(8):58-80,23.基金项目
国家重点研发计划项目(2022YFD2002202) （2022YFD2002202）
青年北京学者计划项目(2024-078). （2024-078）
