Mapping of Soil Properties Using Machine Learning Techniques

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