Determination of soil physicochemical attributes in farming sites through visible, near-infrared diffuse reflectance spectroscopy and PLSR modeling

Abstract

Abstract Introduction An accurate and reliable detection of soil physicochemical attributes (SPAs) is a difficult and complicated issue in soil science. The SPA may be varied spatially and temporally with the complexity of nature. In the past, SPA detection has been obtained through routine soil chemical and physical laboratory analysis. However, these laboratory methods do not fulfill the rapid requirements. Accordingly, diffuse reflectance spectroscopy (DRS) can be used to nondestructively detect and characterize soil attributes with superior solution. In the present article, we report a study done through spectral curves in the visible (350–700 nm) and near-infrared (700–2500 nm) (VNIR) region of 74 soil specimens which were agglomerated by farming sectors of Phulambri Tehsil of the Aurangabad region of Maharashtra, India. The quantitative analysis of VNIR spectrum was done. Results The spectra of agglomerated farming soils were acquired by the Analytical Spectral Device (ASD) Field spec 4 spectroradiometer. The soil spectra of the VNIR region were preprocessed to get pure spectra which were the input for regression modeling. The partial least squares regression (PLSR) model was computed to construct the calibration models, which were individually validated for the prediction of SPA from the soil spectrum. The computed model was based on a correlation study between reflected spectra and detected SPA. The detected SPAs were soil organic carbon (SOC), nitrogen (N), soil organic matter (SOM), pH values, electrical conductivity (EC), phosphorus (P), potassium (K), iron (Fe), sand, silt, and clay. The accuracy of the PLSR model-validated determinant (R 2) values were SOC 0.89, N 0.68, SOM 0.93, pH values 0.82, EC 0.89, P 0.98, K 0.82, Fe 0.94, sand 0.98, silt 0.90, and clay 0.69 with root mean square error of prediction (RMSEP) 3.51, 4.34, 2.66, 2.12, 4.11, 1.41, 4.22, 1.56, 1.89, 1.97, and 9.91, respectively. According to the experimental results, the VNIR-DRS was better for detection of SPA and produced more accurate predictions for SPA. Conclusions In conclusion, the methods examined here offered rapid and novel detection of SPA from reflectance spectroscopy. The outcome of the present research will be apt for precision farming and decision-making.

Keywords

• VNIR reflectance spectroscopy
• First-derivative transformation
• Soil physicochemical attributes
• Partial least squares regression (PLSR)
• Spectral pre-treatment
• Nondestructive technique