Multimodal Sensor Fusion for Non-Destructive Tea Quality Evaluation: Deep Learning-Enabled Methods, Applications, and Challenges
- 1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Abstract
Tea quality evaluation is increasingly moving from subjective sensory assessment and destructive laboratory analysis toward rapid, non-destructive, and data-driven approaches. This review summarizes recent advances in multimodal sensing integrated with deep learning for tea quality evaluation, with emphasis on sensor complementarity, data-fusion strategies, representative applications, and deployment-related limitations. Major sensing modalities, including machine vision, near- and mid-infrared spectroscopy, Raman and fluorescence spectroscopy, hyperspectral imaging, and electronic nose/electronic tongue systems, are discussed in relation to their ability to characterize appearance, chemical composition, aroma, flavor, processing status, and safety-related attributes. Applications are examined for quality grading, chemical composition prediction, aroma and flavor characterization, fermentation monitoring, and safety-related extensions across representative tea products, including green tea, black tea, dark tea, matcha, and jasmine tea. Overall, multimodal approaches can outperform single-sensor systems only when the selected modalities provide complementary, rather than redundant, information layers. However, practical translation remains constrained by small and weakly standardized datasets, insufficient external validation, sensor instability, limited model transferability, high computational cost, and insufficient interpretability. Future research should prioritize standardized datasets, leakage-free validation protocols, interpretable multimodal modeling, truly independent external validation, interoperable multi-sensor platforms, and lightweight deployable models.
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BibTeX
@article{hu2026multimodal,
title = {{Multimodal Sensor Fusion for Non-Destructive Tea Quality Evaluation: Deep Learning-Enabled Methods, Applications, and Challenges}},
author = {Xinyu Hu and Meng Zhang and Biyue Yang and Yuefei Tao and Wei Wei},
journal = {Foods},
year = {2026},
month = may,
doi = {10.3390/foods15101810},
url = {https://doi.org/10.3390/foods15101810},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multimodal Sensor Fusion for Non-Destructive Tea Quality Evaluation: Deep Learning-Enabled Methods, Applications, and Challenges
AU - Xinyu Hu
AU - Meng Zhang
AU - Biyue Yang
AU - Yuefei Tao
AU - Wei Wei
PY - 2026
DA - 2026/05/20
JO - Foods
DO - 10.3390/foods15101810
UR - https://doi.org/10.3390/foods15101810
AB - Tea quality evaluation is increasingly moving from subjective sensory assessment and destructive laboratory analysis toward rapid, non-destructive, and data-driven approaches. This review summarizes recent advances in multimodal sensing integrated with deep learning for tea quality evaluation, with emphasis on sensor complementarity, data-fusion strategies, representative applications, and deployment-related limitations. Major sensing modalities, including machine vision, near- and mid-infrared spectroscopy, Raman and fluorescence spectroscopy, hyperspectral imaging, and electronic nose/electronic tongue systems, are discussed in relation to their ability to characterize appearance, chemical composition, aroma, flavor, processing status, and safety-related attributes. Applications are examined for quality grading, chemical composition prediction, aroma and flavor characterization, fermentation monitoring, and safety-related extensions across representative tea products, including green tea, black tea, dark tea, matcha, and jasmine tea. Overall, multimodal approaches can outperform single-sensor systems only when the selected modalities provide complementary, rather than redundant, information layers. However, practical translation remains constrained by small and weakly standardized datasets, insufficient external validation, sensor instability, limited model transferability, high computational cost, and insufficient interpretability. Future research should prioritize standardized datasets, leakage-free validation protocols, interpretable multimodal modeling, truly independent external validation, interoperable multi-sensor platforms, and lightweight deployable models.
SN - 2304-8158
ER -