Visible near-infrared (Vis-NIR) spectroscopy is widely used for rapid soil element detection, but calibration models are often limited by instrument-specific constraints, including varying laboratory conditions and sensor configurations. To address this, we propose a novel calibration transfer method that eliminates the conventional requirement of designating ‘master’ and ‘slave’ devices. Instead, spectral data from two spectrometers are fused to create a master spectrum, while independent spectral data serve as slave spectra. We developed an ensemble stacking model, incorporating partial least squares regression (PLSR), support vector regression (SVR), and ridge regression (Ridge) in the first layer, with BoostForest (BF) as the second layer, trained on the fused master spectrum. This model was further integrated with three calibration transfer methods: direct standardization (DS), piecewise direct standardization (PDS), and spectral space transfer (SST), to enable seamless application across slave spectra. Applied to soil total nitrogen (TN) detection, the method achieved an R²_P of 0.842, RMSEP of 0.017, and RPD of 2.544 on the first slave spectrometer, and an R²_P of 0.830, RMSEP of 0.018, and RPD of 2.452 on the second. These results demonstrate the method’s ability to simplify calibration processes while enhancing cross-instrument prediction accuracy, supporting robust and generalizable cross-instrument applications.

Rapid and green detection of soil nutrients is essential for soil fertility and plant growth. However, traditional methods cannot meet the needs of rapid detection, and the reagents easily cause environmental pollution. Hence, we proposed a multivariable output weighting-network (MW-Net) combined with laser-induced breakdown spectroscopy (LIBS) to achieve rapid and green detection for three soil nutrients. For a better spectral signal-to-background ratio (SBR), the two important parameters of delay time and gate width were optimized. Then, the spectral noise was removed by the near-zero standard deviation method. Three common quantitative models were investigated for single-element prediction, which are usually applied in LIBS analysis. Also, multi-element prediction was investigated using MW-Net. The results showed that MW-Net outperformed other models generally with very good quantification for soil total N and K (the determination coefficients in the prediction set (R_p²) of 0.75 and 0.83 and the relative percent difference in the prediction sets (RPD) of 2.05 and 2.43) and excellent indirect determination for soil exchangeable Ca (R_p² of 0.93 and RPD of 3.91). Finally, the interpretability was realized through feature extraction from MW-Net, indicating its design rationality. The preliminary results indicated that MW-Net combined with LIBS technology could quantify the three soil nutrients simultaneously, improving the detection efficiency, and it could possibly be deployed on a LIBS portable instrument in the future for precision agriculture.

Rapid assessment of internal fruit quality is essential for fruit grading, postharvest management, and consumer-oriented quality evaluation. Among the quality attributes, soluble solids content (SSC) is a key indicator of citrus sweetness and maturity. Visible and near-infrared (Vis–NIR) spectroscopy provides an effective approach for rapid SSC detection in fruit. However, most existing studies rely on single full-spectrum models or simple band stacking strategies, which limits their ability to fully exploit complementary information among different spectral sub-bands. To address this limitation, a color residual-variance gated attention fusion network (CR-VGAFNet) is proposed for efficient SSC assessment in Hongmeiren. On the independent prediction set, CR-VGAFNet achieved a prediction correlation coefficient (R_P) of 0.7744, a root mean square error of prediction (RMSE_P) of 0.6530 °Brix, and a mean absolute percentage error of prediction (MAPE_P) of 4.83%. These findings suggest the potential of the framework for multi-band spectral fusion. This study provides a new technical perspective for multi-band spectral fusion and rapid fruit quality assessment.

Soil organic carbon (SOC) is a key indicator of soil quality, farmland productivity, and the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy offers a rapid approach for SOC estimation, but wavelength point selection may disrupt continuous spectral structures, whereas conventional wavelength interval selection may fail to fully exploit complementary information across spectral regions. In this study, a synergistic interval-constrained Ridge regression framework, termed sicRidge, was developed for SOC prediction. Continuous candidate intervals were generated using a sliding-window strategy, and a dual-interval synergistic search with an overlap constraint was applied to identify complementary and low-redundancy interval combinations. The selected intervals were then used to construct Ridge regression models. Using Vis-NIR spectra from 168 soil samples, sicRidge was compared with full-spectrum Ridge regression, five wavelength point selection-based Ridge models, and several wavelength interval selection-related benchmark models. sicRidge achieved the best prediction performance using 140 selected bands, with an R²_P of 0.834, RMSE_P of 2.010 g kg⁻¹, RPD of 2.483, and RPIQ of 3.777. The optimal intervals were 570~649 nm and 1880~1939 nm. These results indicate that sicRidge can improve SOC prediction by preserving continuous spectral structures while exploiting complementary cross-region information.