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This research aims to develop a rapid framework for assessing soil pH and nutrient levels in high-latitude tea plantations using hyperspectral imaging and machine learning.

June 9, 2026Open Access

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

Key Points

This research aims to develop a rapid framework for assessing soil pH and nutrient levels in high-latitude tea plantations using hyperspectral imaging and machine learning.
Developed a non-destructive framework integrating hyperspectral imaging and machine learning.
Collected 150 soil samples from three depths (0–20, 20–40, and 40–60 cm) in the Taishan region.
Compared various spectral preprocessing methods, feature-band selection algorithms, and regression models.
Soils were generally acidic with surface enrichment of SOM, AN, AP, and AK, decreasing with depth.
BOSS method performed best for reducing spectral redundancy and improving prediction accuracy.
Optimal SVR models achieved strong predictive performance with correlation coefficients (Rp) of 0.94–0.99 and RPD values of 2.963–10.425.

Abstract

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.

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