Advanced hyper-spectral feature engineering for real-time prediction of tea quality in support of high-grade cultivation

Abstract

Accurate, real-time, and non-destructive monitoring of fresh tea leaf quality is essential for achieving high standards in tea cultivation. This study aimed to develop robust predictive models for key quality components—tea polyphenols, free amino acids, and the polyphenol-to-amino acid ratio (TP/AA)—by integrating hyperspectral reflectance data and meteorological variables. Hyperspectral data were collected from tea canopy using an ASD HandHeld 2 spectrometer across six representative tea gardens in Jiangsu Province, China, during spring, summer, and autumn. Simultaneously, fresh leaf samples were analyzed for biochemical composition, and corresponding meteorological data were recorded. Sensitive spectral features were extracted using harmonic and wavelet transformations, and feature-based hyperspectral indices were constructed via two- and three-feature combination strategies. Three machine learning algorithms—Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Partial Least Squares Regression (PLSR)—were employed to build predictive models. The results revealed significant seasonal variation in quality components, with tea polyphenols and TP/AA peaking in summer and amino acids in spring. Harmonic and wavelet features outperformed raw reflectance in correlating with quality indicators. Models integrating these features achieved high predictive accuracy for tea polyphenols (R² = 0.59–0.71), free amino acids (R² = 0.65–0.79), and TP/AA (R² = 0.60–0.77) across different periods, which further improved (up to R² = 0.86) using RF and LASSO with the inclusion of meteorological variables. The best model performance was observed in autumn, followed by summer and spring. This research proposes an effective machine learning–based remote sensing approach for non-destructive tea quality assessment, offering practical value for precision management and optimized harvest scheduling in high-quality tea production.

Plant phenotyping relevance

茶葉キャノピーのハイパースペクトルから生葉の品質成分を非破壊推定する特徴抽出・機械学習手法を開発し、複数季節・茶園で精度評価しており、表現型取得手法が中心である。

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