College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Sensors Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China.
Molecules. 2022 Mar 21;27(6):2017. doi: 10.3390/molecules27062017.
Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH-N), sodium nitrate (nitrate nitrogen, NO-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH-N, R = 0.92, RMSE = 0.77% and RPD = 3.63; for NO-N, R = 0.92, RMSE = 0.74% and RPD = 4.17; for urea-N, R = 0.96, RMSE = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.
通过检测氮含量快速准确地确定土壤氮供应能力，对指导农业生产活动具有重要意义。本研究采用近红外高光谱成像（NIR-HSI）技术，结合两种光谱预处理算法、两种特征波长选择算法和两种机器学习算法，用于测定土壤氮含量。研究了两种类型的土壤（红土和黄土，均采集于 2020 年）和三种类型的氮肥，即碳酸氢铵（铵态氮，NH-N）、硝酸钠（硝态氮，NO-N）和尿素（尿素氮，urea-N）。对三种类型的氮的 NIR 特征峰进行了赋值，并建立了回归模型。通过比较 100 次运行后的模型平均性能指标，得出了适用于不同类型氮检测的最佳模型。对于 NH-N，R = 0.92，RMSE = 0.77%，RPD = 3.63；对于 NO-N，R = 0.92，RMSE = 0.74%，RPD = 4.17；对于尿素-N，R = 0.96，RMSE = 0.57%，RPD = 5.24。因此，可以得出结论，HSI 光谱结合多元模型适用于土壤中各种土壤 N 的高精度检测。本研究为未来精准农业的发展提供了研究基础。
