DEVELOPMENT AND DEPLOYMENT OF A FIELD BASED SOIL MAPPING TOOL USING A COMPARATIVE EVALUATION OF GEOSTATISTICS AND MACHINE LEARNING

Soil property variability is a large component of the overall environmental variability that Precision Agriculture practices seek to address. Thus, the creation of accurate field soil maps from field soil samples is of utmost importance to practitioners of Precision Agriculture, as understanding and characterizing variability is the first step in addressing it. Today, growers often interpolate their soil maps in a “black-box” fashion, and there is a need for an easy to use, accurate method of interpolation. In this study, current interpolation practices are examined as a benchmark, a Random Forest (RF) based prediction framework utilizes public data to aid predictions, and the RF framework is exposed via a webtool. A high density (0.20 ha/sample) field soil sample dataset provides 28 training points and 82 validation points to be used as a case study. In the prediction of soil percent organic matter (OM), the grid and ordinary kriging interpolations both had higher Mean Absolute Error (MAE) scores than a field average prediction, though the difference was not statistically significant at a 5\% confidence level. A RF framework interpolation utilizing a high resolution (1.52 m) DEM and distances to known points as the feature set had a significantly lower MAE score than the field average, grid, and ordinary kriging interpolations. The results suggest that for the study site, RF framework performed better compared to a field average, a grid based, and an ordinary kriging interpolation methods.