Soil analysis is fundamental to sustainable agriculture; however, traditional laboratory methods are time-consuming, expensive, and environmentally hazardous. Spectroscopy techniques, particularly Near-Infrared (NIR), have gained considerable attention because they require minimal sample preparation, no chemicals, and predict multiple soil properties with a single scan. However, selecting appropriate equipment remains critical, as previous studies have reported inconsistent performance between conventional Near-Infrared (NIR) spectroscopy and advanced Fourier-Transform Near-Infrared (FT-NIR) spectroscopy. Therefore, this study aimed to compare the predictive performance of conventional NIR and advanced FT-NIR spectroscopy for sixteen soil properties. Soil samples (n = 567) were collected from different land-use types across South Korea at a depth of 0–20 cm and analyzed using laboratory methods and spectroscopy techniques. Five models, including partial least squares regression (PLSR), Cubist, support vector machine (SVM), random forest (RF), and memory-based learning (MBL), were evaluated using 15-fold cross-validation to assess prediction accuracy. Overall, conventional NIR spectroscopy yielded consistently higher accuracy for all soil properties than FT-NIR. Strong predictive accuracy was achieved for EC (R² = 0.84), OM (R² = 0.84), avl. P (R² = 0.77), TN (R² = 0.84), and CEC (R² = 0.76). In contrast, FT-NIR provided good prediction accuracy only for Ex. K (R² = 0.72) and TN (R² = 0.84). The average performance of NIR (R² = 0.67) outperformed FT-NIR spectroscopy (R² = 0.63) across all soil properties. These findings demonstrate that, despite their lower spectral resolution, NIR spectra provide robust predictive capability across a wide range of soil properties, which can significantly reduce the investment cost of advanced equipment such as FT-NIR for routine soil analysis.

Infrared (IR) spectroscopy has emerged as a rapid, cost-effective, and reliable alternative to traditional methods, enabling real-time, indirect monitoring of nutrients. Most reviews have discussed visible-near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy individually for soil analysis. This review highlights the application of IR spectroscopy, particularly Vis-NIR, MIR spectroscopy, and their data fusion, coupled with chemometrics and spectral preprocessing for estimating soil attributes. Additionally, the crucial functions of assessing model accuracy and validating model estimates of soil properties are discussed. Partial least squares regression (PLSR) was used in more than 100 studies in 2022. Based on the literature published from 2020 to 2025, the data fusion method predicts soil properties more accurately. This review also sheds light on recent advances in spectroscopic methods, including improvements in speed (e.g., MIR spectroscopy is up to 12 times faster than traditional methods), instrument miniaturization, and integration with portable devices, which can make field analysis more affordable. However, the sensitivity of IR spectroscopy to soil moisture, sample heterogeneity, vegetation cover, and calibration transfer issues remains a significant challenge in certain studies. Therefore, a discussion on the challenges in implementing this technique is included in this review, and future perspectives, such as integration of various sensors and portable devices for real-time soil assessment, are successively discussed.

Greenhouse gas emissions (GHG) are contributing significantly to climate change, particularly from rice paddy fields. However, agricultural management practices such as silicate application can mitigate GHG emissions by altering soil properties and microbial activities. Therefore, the study objective was to analyze the effects of silicate and different fertilizers on GHG emissions in rice paddy fields. The experimental design included five treatments: no fertilizer (NF), inorganic fertilizer (NPK), NPK with rice straw compost (NPKC), NPK with silicate fertilizer (NPKS), and NPK with both rice straw compost and silicate fertilizer (NPKCS). Results demonstrated that the combined application of silicate and compost fertilizers (NPKCS) reduced methane (CH₄) emissions by approximately 14.08% and significantly lowered nitrous oxide (N₂O) (37.15%) emissions compared to NPKC. Additionally, the rice yield increased under all treatments compared to the control (NF), with a greater yield observed under the NPKCS (158.14%). These results underscore the importance of the synergistic use of silicate with compost to mitigate GHG emissions and improve crop productivity.

Limited light intensity and low temperature in winter lead to various challenges such as reduction in growth, yield and quality of cultivated roses, which can be complemented by artificial supplementary lights. This study aims to evaluate the effect of different supplementary lights, including metal-halide (MH), metal-halide+high-pressure sodium lamp (MH + HPS) and high-pressure sodium lamp (HPS) on the growth characteristics of cultivated roses in winter. Compared to individual light, the results demonstrated that combined supplementary lights (MH + HPS) increased stem diameter, number of leaves and flower diameter of cultivated roses. The height, leaf length, leaf width, number of petals, chlorophyll content and chlorophyll fluorescence (Fv/Fm) of roses grown in different supplementary lights were not significantly affected. In all the three treatment areas, photosynthetic photon flux density (PPFD) and temperature at night were higher in the MH + HPS area, followed by the HPS and MH areas. The plant-induced electrical signal (PIES) of roses cultivated under MH + HPS light indicated higher water and nutrient uptake than other treatments, which was positively associated with rose growth, but the difference was insignificant. Principal component analysis (PCA) revealed that the growth parameters of roses were mainly associated with MH + HPS supplementary light. Therefore, combined supplementary light was beneficial to improve the growth and quality of cultivated roses.