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He Y, Xiao S, Nie P, et al. Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor. Sensors (Basel). 2017;17(9)doi: 10.3390/s17092045.
He, Y., Xiao, S., Nie, P., Dong, T., Qu, F., & Lin, L. (2017). Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor. Sensors (Basel, Switzerland), 17(9), . https://doi.org/10.3390/s17092045
He, Yong, et al. "Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor." Sensors (Basel, Switzerland) vol. 17,9 (2017). doi: https://doi.org/10.3390/s17092045
He Y, Xiao S, Nie P, Dong T, Qu F, Lin L. Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor. Sensors (Basel). 2017 Sep 07;17(9). doi: 10.3390/s17092045. PMID: 28880202; PMCID: PMC5621142.
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Sensors (Basel). 2017 Sep 07;17(9). doi: 10.3390/s17092045.

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

Sensors (Basel, Switzerland)

Yong He, Shupei Xiao, Pengcheng Nie, Tao Dong, Fangfang Qu, Lei Lin

Affiliations

  1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. [email protected].
  2. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. [email protected].
  3. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. [email protected].
  4. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. [email protected].
  5. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. [email protected].
  6. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. [email protected].
  7. State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China. [email protected].
  8. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. [email protected].
  9. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. [email protected].
  10. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. [email protected].
  11. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. [email protected].
  12. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. [email protected].
  13. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. [email protected].

PMID: 28880202 PMCID: PMC5621142 DOI: 10.3390/s17092045

Abstract

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.

Keywords: PLS; UVE; drying time; near infrared sensor; nitrogen; water content

Conflict of interest statement

The authors declare no conflict of interest.

References

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  2. Sensors (Basel). 2013 Aug 16;13(8):10823-43 - PubMed
  3. Sensors (Basel). 2017 May 11;17 (5):null - PubMed

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