Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices.

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.

Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop status by detecting deficiencies in essential nutrients. This study evaluated the effects of N and P deficiency on field grown broccoli (Brassica oleracea L. var. italica Plenk) using a plant-induced electrical signal (PIES) sensor, in which needle electrodes are inserted into the stem to measure electrical conductivity reflecting plant water and ion status. Four treatments were established, including the control (N100P100) with sufficient N and P supply, N deficiency (N0P100), P deficiency (N100P0), and combined N–P deficiency (N0P0). For sufficient supply, urea and fused phosphate (FP) were applied at rates of 122 kg N ha⁻¹ and 71 kg P ha⁻¹, respectively. Soil, stem, and leaf nutrient contents, growth parameters, and stress related indicators were analyzed and their relationship with PIES values were evaluated. PIES was highest in control (N100P100) and lowest under N–P deficiency (N0P0). Higher PIES values were observed during the vegetative stage, whereas values declined during the reproductive stage, reflecting changes in physiological activity. Growth parameters such as shoot and root weight and stem diameter were generally superior in the control (N100P100) treatment, while leaf calcium (Ca), magnesium (Mg), and potassium (K) concentrations showed no significant differences among treatments. Total N content in leaves was higher in N fertilized treatments (control and P deficiency). Photosynthesis-related parameters, including soil plant analysis development (SPAD), Fv/Fm, and chlorophyll content, were lowest under N–P deficiency, which was reflected in the PIES. Principal component analysis (PCA) showed that the PIES was closely associated with growth and photosynthetic parameters and clearly distinguished N sufficient treatments (control and P deficiency) from N deficient treatments (N0P100, N0P0). Overall, these findings suggest that PIES monitoring can serve as a sensitive physiological indicator of nutrient stress and may be applied as an early diagnostic tool before visible growth inhibition occurs in broccoli cultivation.

Improving nutrient use efficiency and minimizing environmental pollution from excessive fertilization require appropriate nutrient management supported by continuous monitoring of soil nutrient levels during crop growth. As only a few real-time sensors for the measurement of soil nutrients are available, this study evaluated the potential of electrical conductivity (EC) sensors, which reflect the ionic concentrations of the soil solution, for real-time estimation of plant-available nutrient levels. Nitrogen and potassium were sequentially supplied to achieve cumulative application rates of 25–300% of the nutrient uptake-based fertilization rate. The relationship between cumulative fertilization rate and accumulated sensor-based EC increase was described using linear, polynomial, and nonlinear saturation models. Sensor EC increased linearly from 25 to 125% of the nutrient uptake-based fertilization rate, while higher application rates were better explained by the nonlinear saturation equation. Sensor-based EC showed strong correlation with soil ammonium nitrogen (NH₄⁺-N), indicating that the sensor effectively reflected nutrient dynamics. In open-field pepper soil, fertigation-induced increases in sensor EC followed the patterns predicted by both the linear and nonlinear saturation models established in the laboratory. These results demonstrate that EC sensors can be used for real-time monitoring of soil nutrient levels and may contribute to efficient nutrient management in open-field cultivation.