Deep-Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data

One of the vital growth nutrient parameters of crops is soil Nitrogen (N) content. The ability to accurately grasp soil nutrient information is a prerequisite for scientific fertilization within the field of precision agriculture. Information pertaining to soil macronutrients, such as N, may be obtained quickly through hyperspectral imaging techniques. Objective of this research is to explore the use of a deep learning (DL) network to estimate the abundance of urea fertilizer mixed soils for spectroradiometer data. The proposed approach was tested for silt clay and loamy types of soils. Spectral regions of 1899.2 nm for urea and 2195.1 nm for soils were identified as optimum spectral absorption features. The accuracy evaluation was performed using a linear regression model between actual and estimated abundances. At 1899.2 nm, the coefficient of determination (R
²
) for mixed samples of urea and silt clay soil was found to be 0.945, while R
²
for urea mixed loamy soil were 0.954. Similarly, at 2195.1 nm, R
²
obtained 0.953 for urea mixed silt clay soil and 0.944 for urea mixed loamy soil. The results show that the estimated abundances obtained through the derivative analysis for spectral unmixing (DASU)-based DL network facilitated a greater accuracy in comparison to the sole use of DASU. These results were then verified through conventional chemical analysis methods. The outcome of this article determines the abundance of urea mixed soils. Therefore, it is inferred that the hyperspectral imaging technique may be utilized in-situ to assess the agricultural land's soil fertility status.