Tea quality assessment is a fundamental component of precision agriculture because it directly influences harvesting decisions, commercial value, processing efficiency, and final beverage quality. Conventional quality evaluation techniques primarily depend on destructive laboratory analyses, manual visual inspection, and expert experience, which often introduce subjectivity, increase operational costs, and limit large-scale monitoring capabilities. Recent developments in remote sensing, vegetation indices, polarization imaging, and optical characterization techniques have created opportunities for non-destructive and automated tea leaf assessment systems. Among these approaches, the integration of Degree of Polarization (DoP), Leaf Area Index (LAI), Photosynthetically Active Radiation (PAR), and the Normalized Difference Vegetation Index (NDVI) offers complementary information regarding leaf morphology, canopy architecture, physiological condition, and photosynthetic performance. These parameters collectively provide a comprehensive representation of tea leaf quality that cannot be achieved through a single indicator.

This review-based study proposes a conceptual multispectral vegetation index framework for intelligent tea leaf quality assessment by synthesizing the findings reported in the provided literature. The framework integrates optical polarization characteristics with vegetation index analysis to establish a multidimensional evaluation strategy for monitoring tea leaf growth and determining harvest readiness. The paper critically reviews previous developments in remote sensing, bidirectional reflectance distribution function (BRDF) modeling, Monte Carlo simulation, vegetation monitoring, optical modeling, and tea quality estimation while identifying remaining research gaps. Furthermore, it discusses the theoretical relationships among polarization measurements, canopy structural properties, solar radiation interactions, and vegetation indices in the context of intelligent agricultural monitoring.

The proposed framework demonstrates how multispectral observations can improve decision support for tea cultivation by enabling continuous, objective, and non-destructive quality estimation. The review concludes that integrating optical polarization with vegetation indices represents a promising direction for next-generation precision tea farming systems capable of improving harvesting accuracy, resource management, and agricultural sustainability.