Hyperspectral imaging and machine learning for rapid sensing and visualized retrieval of soil nutrients in high-latitude tea-growing regions

Introduction: Rapid assessment of soil pH and nutrient status in tea plantations is essential for precision fertilisation and ecological management, particularly in high-latitude tea-growing regions where related applications remain insufficiently studied. Methods: This study developed a rapid, non-destructive framework integrating hyperspectral imaging and machine learning for the detection, retrieval, and spatial visualisation of soil pH, soil organic matter (SOM), alkali-hydrolysable nitrogen (AN), available phosphorus (AP), and available potassium (AK) in the Taishan tea-producing region. A total of 150 soil samples were collected from three profile depths (0-20, 20-40, and 40-60 cm). Hyperspectral images were acquired over 394-1007 nm, and the 481-908 nm range was retained for modelling. Principal component analysis was used to characterise vertical differentiation, while four spectral pre-processing methods, three feature-band selection algorithms-competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), and successive projections algorithm (SPA)-and three regression models-partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR)-were systematically compared. Results: The soils were generally acidic, and SOM, AN, AP, and AK exhibited clear surface enrichment and decreasing trends with increasing depth. Among the feature-selection methods, BOSS showed the best overall performance in reducing spectral redundancy and improving prediction accuracy. The optimal SVR models, combined with parameter-specific pre-processing and BOSS-selected bands, achieved strong predictive performance across all indicators, with prediction-set correlation coefficients (Rp) of 0.94-0.99 and relative percent deviation (RPD) values of 2.963-10.425. Furthermore, pixel-wise reconstruction using threshold masking enabled intuitive two-dimensional visualisation of the spatial distributions of the target soil properties. Discussion: These results demonstrate that hyperspectral imaging coupled with machine learning provides an effective approach for rapid soil nutrient assessment, spatial visualisation, and digital management in high-latitude tea plantations.