On-demand fertilization does not only help to improve fertilizer use efficiency, but also to avoid over-use of chemical fertilizers. Rapid monitoring of the nutrient content constitutes the initial step of on-demand fertilization, and spectroscopy technology is considered as one of the most ideal nondestructive methods to detect the nutrient content. The typical soil spectra contain thousands of wavelengths, these spectral variables often contribute to collinearity and redundancies rather than relevant effective information. To resolve this issue, this paper selected soil total nitrogen (STN) content as the study subject, and the studies were carried out from two aspects, STN characteristic wavelengths screening and STN content prediction model building based on the limited number of independent variables. For the characteristic waveband screening process, four feature extraction methods (including F-test, mutual information, embedded method and deconvolution operation) were used and the results were compared and analyzed. The embedded method was selected as the benchmark method due to its advantages of simple, intuitive and reliable to screen the STN characteristic wavelengths. In the process of building the STN content prediction model, under-fitting problem is a major challenge which is driven by the limitation of soil nitrogen characteristic wavelengths. Towards this, this study proposed a method for constructing complex features for predicting STN content based on convolution operations which shows higher accuracy than those based on Multi-Layer Perceptron Neural Network and polynomial kernel functions. When the number of characteristic wavelengths is 16, the coefficient of determination (R²) is 0.69, root mean square error of prediction (RMSEP) is 4.34 g/kg, and the residual prediction deviation (RPD) is 0.86. After construction with convolution operations (256 features are calculated from these original 16 characteristic wavelengths), the R² of the model reaches 0.86, the RMSEP is 1.98 g/kg, and the RPD is 1.89. This study provides a practical approach to extract target characteristics from soil spectra and enhance the relevant information for detecting STN based on the raw soil spectra.

按需施肥不仅有助于提高肥料的使用效率，而且可以避免化肥的过度使用。快速监测养分含量构成了按需施肥的第一步，光谱技术被认为是最理想的无损检测养分含量的方法之一。典型的土壤光谱包含数千个波长，这些光谱变量通常会导致共线性和冗余，而不是相关的有效信息。针对这一问题，本文选择土壤全氮（STN）含量作为研究对象，从STN特征波长筛选和基于有限自变量数量的STN含量预测模型构建两个方面进行研究。在特征波段筛选过程中，采用了四种特征提取方法（包括F检验、互信息、嵌入方法和反卷积操作），并对结果进行了比较和分析。嵌入法因其筛选STN特征波长简单、直观、可靠等优点而被选为基准法。在建立 STN 含量预测模型的过程中，欠拟合问题是受土壤氮特征波长限制驱动的主要挑战。为此，本研究提出了一种基于卷积运算构建复杂特征以预测 STN 内容的方法，其精度高于基于多层感知器神经网络和多项式核函数的方法。² )为0.69，预测均方根误差(RMSEP)为4.34 g/kg，残差预测偏差(RPD)为0.86。经过卷积操作构建后（这16个特征波长计算出256个特征），模型的R ²达到0.86，RMSEP为1.98 g/kg，RPD为1.89。本研究提供了一种从土壤光谱中提取目标特征的实用方法，并增强了基于原始土壤光谱检测 STN 的相关信息。