Application of Visible/Near Infrared Spectrometers to Quickly Detect the Nitrogen, Phosphorus, and Potassium Content of Chemical Fertilizers

文献类型： 外文期刊

第一作者： Shen, Jiangang

作者： Shen, Jiangang;Zhou, Jun;Liu, Fei;Qiao, Weiming;Chen, Huizhe;Liu, Fei

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关键词： chemical fertilizer; nitrogen; phosphorus; potassium; visible spectroscopy; near-infrared spectroscopy

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

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年卷期： 2021 年 11 卷 11 期

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收录情况： SCI

摘要： The rapid determination of nitrogen, phosphorus, potassium and other major nutrient elements is an important technical guarantee in the quality control of chemical fertilizers. In this study, a small visible spectrometer and a small near-infrared spectrometer were used to collect spectrum information of 33 different common chemical fertilizers including compound fertilizers, blended fertilizers and controlled-release fertilizers. The 550 similar to 950 nm and 1050 similar to 1640 nm spectra with stable signals were intercepted as the analysis spectrum, and the competitive adaptive reweighted sampling algorithm (CARS) was used to select 161, 229, and 161 spectral characteristic wavelengths for the three nutrient contents of N, P2O5, and K2O respectively. The partial least squares (PLS) and extreme learning machine (ELM) models of N, P2O5, and K2O were established based on the 550 similar to 950 nm waveband, 1050 similar to 1640 nm waveband, full spectrum, and characteristic wavelength, respectively. The coefficient of determination (R-2), root mean square error (RMSE), and residual predictive deviation (RPD) were used to evaluate the effect of the model. With the optimal prediction models, the values of R-p(2) for N, P2O5, and K2O were 0.989, 0.963, 0.981, and for RPD were 9.71, 5.09, 7.29, respectively. The research results show that Vis/NIR spectroscopy can predict the content of nitrogen, phosphorus, and potassium nutrients in fertilizers, and the near-infrared band from 1050 nm to 1640 nm has a better prediction effect. The characteristic wavelength selection reduces the spectral variables by 9/10, and the performance of the model based on the characteristic wavelength is close to that of the full-spectrum model.

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