Zhou Ling, Yao Jiangjun, Xu Honggang, Zhang Yahui, Nie Pengcheng
College of Information Engineering, Tarim University, 1188 Junken Avenue, Alar 843300, China.
Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, 1188 Junken Avenue, Alar 843300, China.
Molecules. 2023 Sep 7;28(18):6507. doi: 10.3390/molecules28186507.
Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.
硝态氮在土壤养分循环中起着重要作用，近红外光谱能够高效、准确地检测土壤中硝态氮的含量。因此，通过深入研究土壤中养分的循环和转化模式，可为土壤改良和农业生产力提供科学依据。为了研究干燥温度对近红外土壤氮检测的影响，分别将不同氮浓度的土壤样品在50℃、65℃、80℃和95℃下干燥。此外，将在室温（25℃）下自然风干的土壤样品作为对照组。根据干燥温度调整不同的干燥时间，以完全消除水分的影响。在用近红外光谱仪收集数据后，选择最佳的预处理方法来处理原始数据。基于随机蛙跳算法（RFFS）、竞争性自适应重加权算法（CARS）和连续投影算法（SPA）选择的特征波段，建立了偏最小二乘回归（PLSR）和支持向量机（SVM）两种线性模型以及人工神经网络（ANN）非线性模型进行分析比较。结果发现，干燥温度对近红外光谱法检测土壤氮有很大影响。同时，SPA-ANN模型的准确性最佳且最稳定，决定系数Rc2 = 0.998，预测决定系数Rp2 = 0.989，校正均方根误差RMSEC = 0.178 g/kg，预测均方根误差RMSEP = 0.257 g/kg。结果表明，在80℃土壤干燥温度下，近红外光谱法检测氮的影响最小且准确性最高。这项工作为未来的农业生产提供了理论基础。
