Prediction of Salt in Soil by PLS Regression Using Hyperspectral Laboratory Data - Padghan, Tejas U; Deshmukh, Ratnadeep R; Katye, Jaypalsing N; Khandizod, Anita G; Janse, Pooja V (International Journal of Computer Sciences and Engineering, 2019)
the intent of this paper is prediction of the salt content in agriculture soil. Soil salinity is a process which affects the quality of soil and reduces the agriculture production. The soil salt content adversely affects the soil physical property including soil water content. The Visible and Near-Infrared Reflectance Spectroscopy provides improved estimation of soil salinity as fast approach to the characterization of soil salt content with spectral resolution of 350-2500 nm. The Partial Least Square Regression Method (PLSR) is frequently used to determine Soil Salt Content (SSC) obtains from the spectral data. The Result shows that the 550nm, 850 nm, 1430nm, 1918nm, 2052nm wavelength which are sensitive to salt content and model based on Partial Least Square Regression PLSR can only make approximate predictions for First Derivative RMSE (Root Mean Square Error) = 0.0282-0.0365, R2 (Coefficient of Determination) = 0.9313-0.9051 and for Continuum Remove RMSE (Root Mean Square Error) = 0.0280-0.0386, R2 (Coefficient of Determination) = 0.9313-0.8939.