Shin Su Kyeong, Lee Seung Jun, Park Jin Hee
Department of Environmental and Biological Chemistry, Chungbuk National University, Cheongju 28644, Chungbuk, Republic of Korea.
Sensors (Basel). 2025 Aug 14;25(16):5045. doi: 10.3390/s25165045.
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.
稳定的作物产量需要在准确诊断土壤养分状况的基础上，适当供应氮（N）、磷（P）和钾（K）等必需的土壤养分。传统的土壤养分实验室分析通常复杂且耗时，无法提供实时养分状况。可见-近红外（Vis-NIR）光谱已成为一种无损且快速的土壤养分水平估算方法。Vis-NIR光谱以峰值强度反映样品特征；然而，它们常常受到各种伪像和复杂变量的影响。由于Vis-NIR光谱不直接测量土壤中的养分水平，提高估算精度至关重要。对于光谱预处理，最重要的方面是根据数据特征制定合适的预处理策略，并识别光谱数据中的噪声、基线漂移和散射等伪像。基于机器学习的建模技术，如偏最小二乘回归（PLSR）和支持向量机回归（SVMR），通过捕捉光谱数据的复杂模式提高估算精度。因此，本综述重点关注Vis-NIR光谱在评估土壤性质（包括土壤含水量、有机碳（C）和养分）方面的应用，并通过光谱预处理和机器学习算法探索其在实时田间应用的潜力。预计Vis-NIR光谱与机器学习相结合能够实现更高效、因地制宜的养分管理，从而促进可持续农业实践。
