Mustaqimah Mustaqimah, Yenni Fitri, Devianti Devianti, Agus Arip Munawar
This research aims to develop a Near Infrared Reflectance Spectroscopy (NIRS) model to determine the content of macro nutrients in dry agricultural land. The macro nutrient content is needed by plants to grow ideally. Every plant requires large amounts of macro nutrients such as nitrogen (N), phosphorus (P), and potassium (K). NIR spectrum data were collected from 30 samples in the range of 400-1100 nm using the Nicolet-Antaris device. The dual spectrum techniques used were Standard Normal Variate (SNV) and Mean Normalization (MN). This pre-treatment is chosen based on its function and aims to reduce or eliminate noise in the resulting spectrum. The corrected spectrum pattern helps to reduce discrepancies between spectral bands. The fewer gaps in the spectrum, the more accurate the resulting model. Next, the Partial Least Squares (PLS) and Principal Component Regression (PCR) algorithms are used to form a validation model. This multivariate analysis was carried out using the Unscrambler X 10.3 software. Model reliability is assessed using a number of statistical measures: correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE) and range of error ratio (RER). The best model was taken based on previous findings when applying PCR to the MN-normalized spectra which was superior in predicting nitrogen and potassium content, while the Phosphorus content was superior when applying PCR with the SNV normalized spectrum technique. These findings show that NIRS combined with chemometric can be used to predict the nutritional content of nitrogen, Phosphorus and potassium quickly and simultaneously. ©2025 The authors.
Department of Agricultural Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
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