Digital Sensing for Comprehensive Tea Quality Evaluation: From Dry Tea to Tea Infusion and Infused Leaves

Rapid advances in artificial intelligence, sensor technologies, and image recognition have accelerated the transition toward digital and intelligent systems for evaluating tea quality. Conventional sensory assessment remains the foundation of tea grading and pricing, but it is subjective, labor-intensive, time-consuming, and difficult to standardize for large-scale industrial applications. This review systematically summarizes recent advances in digital sensing technologies for comprehensive tea quality evaluation by organizing current research around three complementary tea quality evaluation objects, namely dry tea, tea infusion, and infused leaves. For dry tea, computer vision, near-infrared spectroscopy (NIRS), and hyperspectral imaging are reviewed for evaluating appearance, morphology, and internal chemical composition. For tea infusion, electronic nose, electronic tongue, spectroscopy, and imaging techniques are discussed for characterizing liquor color, aroma, taste, and physicochemical properties. For infused leaves, computer vision and hyperspectral imaging are summarized for assessing morphology, color, structural characteristics, and their potential value in processing quality verification. The review further highlights multimodal data fusion as a key strategy for integrating complementary information from different sensing modalities and tea quality evaluation objects, thereby improving the accuracy, robustness, and interpretability of digital tea quality assessment. Current challenges, including data heterogeneity, limited model generalization, insufficient research on infused leaves, the lack of standardized protocols and databases, sensor drift, calibration transfer, and equipment cost, are also discussed. Finally, future perspectives are presented for the development of portable, intelligent, and standardized digital sensing systems. Overall, this review provides a comprehensive overview of digital sensing technologies and offers future perspectives for developing objective, traceable, and intelligent tea quality evaluation throughout the tea industry.