Prediction of Soil Nitrogen from Spectral Features Using Supervised Self Organising Maps
Soil Total Nitrogen (TN) can be measured with on-line visible and near infrared spectroscopy (vis-NIRS), whose calibration method may considerably affect the measurement accuracy. The aim of this study was to compare Principal Component Regression (PCR) with Supervised Self organizing Maps (SSOM) for the calibration of a visible and near infrared (vis-NIR) spectrophotometer for the on-line measurement of TN in a field in a German farm. A mobile, fiber type, vis-NIR spectrophotometer (AgroSpec from tec5 Technology for Spectroscopy, Germany) mounted in an on-line sensor platform, comprising of measurement range of 305–2200 nm was utilized so as to obtain soil spectra in diffuse reflectance mode. Both PCR and SSOM calibration models of TN were validated with independent validation sets. The obtain root mean square error (rmse) was equal to 0.0313.The component maps of SSOM allow for a visualization of different correlations between spectral components and nitrogen content.
KeywordsPrecision farming Remote sensing Neural networks Machine learning Optical sensing
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