Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

[1]  C. Feller,et al.  Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity , 2007 .

[2]  B. Minasny,et al.  Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy , 2008 .

[3]  S. Atanassova,et al.  Estimation of total N, total P, pH and electrical conductivity in soil by near-infrared reflectance spectroscopy. , 2011 .

[4]  W. Cai,et al.  A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra , 2008 .

[5]  Rebecca L. Whetton,et al.  Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy , 2016 .

[6]  Annia García Pereira,et al.  Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques , 2006 .

[7]  Ken-ichiro Suehara,et al.  Simultaneous Measurement of Carbon and Nitrogen Content of Compost Using near Infrared Spectroscopy , 2001 .

[8]  Luis Miguel Contreras-Medina,et al.  A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances , 2013, Sensors.

[9]  Yaolin Liu,et al.  Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy , 2014 .

[10]  R. Poppi,et al.  Use of Radial Basis Function Networks and Near-Infrared Spectroscopy for the Determination of Total Nitrogen Content in Soils from Sao Paulo State , 2008, Analytical sciences : the international journal of the Japan Society for Analytical Chemistry.

[11]  Qu Na Application of Near-and Mid-infrared Diffuse Reflectance Spectroscopic Techniques in Soil Analysis , 2015 .

[12]  Hong Sun,et al.  Spectral feature extraction and modeling of soil total nitrogen content based on NIR technology and wavelet packet analysis , 2010, Asia-Pacific Remote Sensing.

[13]  A. Mouazen,et al.  Non-biased prediction of soil organic carbon and total nitrogen with vis–NIR spectroscopy, as affected by soil moisture content and texture , 2013 .

[14]  Bao Yidan Rapid detection method of soil organic matter contents using visible/near infrared diffuse reflectance spectral data , 2011 .

[15]  Mohammad Reza Mobasheri,et al.  Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels , 2016 .

[16]  Guofeng Wu,et al.  Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy , 2012, Plant and Soil.

[17]  Di Wu,et al.  Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver , 2011 .

[18]  C. Feller,et al.  Determination of Total Carbon and Nitrogen Content in a Range of Tropical Soils Using near Infrared Spectroscopy: Influence of Replication and Sample Grinding and Drying , 2006 .

[19]  Deli Chen,et al.  Comparison of Sequential Indicator Simulation and Transition Probability Indicator Simulation Used to Model Clay Content in Microscale Surface Soil , 2009 .

[20]  Minzan Li,et al.  Soil nitrogen content forecasting based on real-time NIR spectroscopy , 2016, Comput. Electron. Agric..

[21]  Svante Wold,et al.  Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and lignin sulfonate , 1983 .

[22]  B. Wesemael,et al.  Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy , 2013 .

[23]  M. Martín,et al.  Partial least-squares method in analysis by differential pulse polarography. Simultaneous determination of amiloride and hydrochlorothiazide in pharmaceutical preparations , 1999 .

[24]  H. Ramon,et al.  Towards development of on-line soil moisture content sensor using a fibre-type NIR spectrophotometer , 2005 .

[25]  Tao Dong,et al.  Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms , 2017, Sensors.

[26]  Song Haiyan,et al.  Determination of organic matter contents and pH values of soil using near infrared spectroscopy , 2008 .

[27]  Paul Geladi,et al.  An example of 2-block predictive partial least-squares regression with simulated data , 1986 .