Du Xinrong, Chen Huazhou, Xie Jun, Li Linghui, Cai Ken, Meng Fangxiu
School of Mathematics and Statistics, Guilin University of Technology, Guilin 541004, China.
School of Mathematics and Statistics, Guilin University of Technology, Guilin 541004, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Jan 5;324:124998. doi: 10.1016/j.saa.2024.124998. Epub 2024 Aug 18.
Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.
土壤钾素是作物生长必需的关键营养元素，其有效测定对于制定合理施肥计划和优化作物生长效益至关重要。目前，基于近红外（NIR）光谱分析的数据挖掘技术已被证明是实时监测土壤钾含量的有力工具。然而，随着技术和仪器的改进，维度问题的困扰也相应增加。因此，迫切需要开发适用于NIR光谱分析技术的高效变量选择方法。在本研究中，我们提出了一种三步渐进式混合变量选择策略，该策略充分利用了几种高性能变量选择方法的各自优势。通过将协同区间偏最小二乘法（SiPLS）、随机森林变量重要性度量（RF(VIM)）和改进的平均影响值算法（IMIV）依次应用于一个融合框架中，提出了一种土壤重要钾变量选择方法，称为SiPLS-RF(VIM)-IMIV。最后，将优化后的变量拟合到偏最小二乘（PLS）模型中。实验结果表明，嵌入混合策略的PLS模型在降低模型复杂度的同时有效提高了预测性能。测试集上的RMSET和RT分别为0.01181%和0.88246，优于全光谱PLS、SiPLS和SiPLS-RF(VIM)方法的RMSET和RT。本研究表明，基于近红外光谱数据与SiPLS-RF(VIM)-IMIV方法相结合建立的混合策略能够定量分析土壤钾含量水平，并有可能解决数据驱动的土壤动态监测的其他问题。
