Integrating Hyperspectral Feature Mining and Explainable Machine Learning for Robust Year-Round Monitoring of Nitrogen Accumulation in Tea Plants

Tea plant leaf nitrogen (N) status directly determines tea yield and quality, and hyperspectral remote sensing enables non-destructive monitoring of these N traits. However, targeted mining of N-sensitive hyperspectral features and interpreting their roles in monitoring models are crucial for achieving stable, year-round tea plant growth monitoring and precision management. To address this, this research proposed a method based on continuous wavelet transform and harmonic decomposition to extract N-sensitive hyperspectral features. Furthermore, interpretable machine learning was employed to elucidate the explicit contributions of these hyperspectral features, thereby establishing a year-round quantitative monitoring model for tea plant N nutrition. Canopy hyperspectral reflectance data (R) of tea plants was collected from 2022 to 2024 concurrently with measurements of leaf N accumulation (LNA). Wavelet features (WF) and harmonic components (HC) were extracted from the hyperspectral data using the continuous wavelet transform and harmonic decomposition, respectively. Multiple machine learning algorithms were employed to fuse multiple categories of sensitive features to establish quantitative monitoring models for LNA in tea plants, with SHapley Additive exPlanations (SHAP) analysis further applied to decode the decision-making mechanism by quantifying individual feature contributions. The results indicated that WF (r = 0.35–0.51) and HC (r = 0.48–0.78) exhibited higher sensitivity to LNA compared to R. Three-feature indices (R2 = 0.50–0.72) significantly improved the monitoring accuracy of leaf N status. The convolutional neural network exhibited consistently robust predictive performance for tea plant N accumulation (R2 = 0.70–0.80). SHAP interpretation further revealed that the core spectral features driving LNA maintained a consistent positive impact across all seasons, with MNDSI(C5, B8) in spring, MMTCI(C51, A2, B126) in summer (mean absolute SHAP = 2.653), and MDCNI(φ10, φ4, φ197) in autumn making dominant contributions. Meanwhile, model-based spatial mapping revealed spatiotemporal heterogeneity (CV = 17.16–31.07%) in N accumulation status across two representative tea plantations. This study establishes a technical framework to quantify the N nutrition status of tea plants, enabling precise, site-specific management and targeted nutrient diagnosis.