Journal
SN APPLIED SCIENCES
Volume 2, Issue 9, Pages -Publisher
SPRINGER INT PUBL AG
DOI: 10.1007/s42452-020-03322-9
Keywords
Soil nitrogen; Partial least squares regression; First-derivative transformation; Reflectance spectroscopy; Agricultural soil
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Funding
- UGC [3-42/2009, 4-15/2015/DRS-II]
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In the present paper, the novel hyperspectral model was developed for the estimation of Soil Nitrogen (SN) in agricultural lands using Partial Least Squares Regression (PLSR) method. In this regard, an effort has been made on predicting and analyzing SN from several agricultural lands of Phulambri Tehsil of Aurangabad district of Maharashtra, India. The spectra of seventy four (74) agricultural soil samples were acquired between 350-2500 nm by Analytical Spectral Device Field Spec-4 Spectroradiometer under controlled laboratory conditions. The preprocessing was done on acquired spectra by First-derivative Transformation (FDT) and Savitzky-Golay (SG) method for getting suitable information. The PLSR approach was derived from correlation analysis between reflectance spectra and SN features. The resulted coefficient of determination (R-2) values was 0.68 and 0.94 before and after pre-treatment with root mean square error of prediction (RMSEP) 4.34 and 1.56, respectively.The identified sensitive wavelength bands of nitrogen content were 480 nm, 511 nm, 653 nm, 997 nm, 1472 nm, 1795 nm, 2210 nm and 2296 nm. In the conclusion, the model is reliable for prediction of SN from agricultural areas. The present research will be useful for decision making in agricultural management.
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