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Research Article Open access CC BY 4.0

Mapping of Soil Properties Using Machine Learning Techniques

S. Sridevy, M. Nivas Raj, P. Kumaresan, N. Balakrishnan, M. Tilak, J. Arockia Stephen Raj, P. Jona Innisai Rani

International Journal of Environment and Climate Change · pp. 684–700 · Published 2 Jun 2023

10.9734/ijecc/2023/v13i81997

Abstract

We aimed to estimate Soil Nutrients and relate the spectral signatures to that of the Laboratory reference Measurements utilizing CART analysis. Sustainable agriculture aims at controlled and/or precise soil fertility interventions based on spatial soil information. The profound advancements in remote sensing and geospatial techniques provide means for determining the spatial coverage and variability of the soil properties through the survey and image data incorporated in the mapping procedures (i.e.) Digital Soil Mapping. The soil moisture content at varying levels influences crop growth and decides the yield, as the crop requires water at critical crop growth stages.  Machine learning techniques provide the means of optimized model calibration when compared to conventional geostatistical or statistical approaches.

CART analysis geostatistical technique machine learning techniques soil properties mapping

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