Method And Apparatus For Mapping Distribution of Chemical Compounds in Soil

A method for mapping distribution of chemical compounds in soil, the method comprising the steps of:

• inserting a probe into the soil at multiple locations;
• obtaining spectroscopic data regarding the soil;
• sampling a core of soil adjacent to the probe locations;
• dividing the core into multiple depth increments;
• analyzing the core samples;
• matching each core sample with a corresponding depth increment of the probe insertions;
• obtaining data from the probe insertions;
• dividing the probe insertion data into training, validation, and test categories;
• resampling spectral variables from the probe insertion data to a wavelength interval longer than a native wavelength interval of an associated spectrometer;
• normalizing the probe insertion data on a spectrum by spectrum basis, utilizing a machine learning normalization algorithm;
• standardizing the spectral variables to a common scale by removing a mean and scaling to unit variance;
• and
• choosing a model utilizing the test set.

A non-transitory computer readable storage device storing computer executable instructions that when executed by a computer controls the computer to perform a method comprising:

• inserting a probe into the soil at multiple locations;
• obtaining spectroscopic data regarding the soil;
• sampling a core of soil adjacent to the probe locations;
• dividing the core into multiple depth increments;
• analyzing the core samples;
• matching each core sample with a corresponding depth increment of the probe insertions;
• obtaining data from the probe insertions;
• dividing the probe insertion data into training, validation, and test categories;
• resampling spectral variables from the probe insertion data to a wavelength interval longer than a native wavelength interval of an associated spectrometer;
• normalizing the probe insertion data on a spectrum by spectrum basis, utilizing a machine learning normalization algorithm;
• standardizing the spectral variables to a common scale by removing a mean and scaling to unit variance;
• and
• choosing a model utilizing the test set.