US 12,031,906 B2
Method and apparatus for mapping distribution of chemical compounds in soil
Linda Barrett, Fairlawn, OH (US)
Assigned to S4 Mobile Laboratories, LLC
Filed by S4 Mobile Laboratories, LLC, Akron, OH (US)
Filed on Jun. 8, 2023, as Appl. No. 18/331,370.
Claims priority of provisional application 63/366,028, filed on Jun. 8, 2022.
Prior Publication US 2023/0400408 A1, Dec. 14, 2023
Int. Cl. G01N 21/3563 (2014.01); G01N 21/359 (2014.01); G01N 21/85 (2006.01); G01N 33/24 (2006.01); G01S 19/26 (2010.01)
CPC G01N 21/3563 (2013.01) [G01N 21/359 (2013.01); G01N 21/8507 (2013.01); G01N 33/24 (2013.01); G01S 19/26 (2013.01); G01N 2021/855 (2013.01); G01N 2201/0636 (2013.01); G01N 2201/0638 (2013.01); G01N 2201/08 (2013.01)] 19 Claims
OG exemplary drawing
 
1. 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;
utilizing a global navigation satellite system to record the locations of the probe;
measuring a depth the probe was inserted into the soil for at least two of the multiple locations;
measuring a pressure at which the probe was inserted into the soil for at least two of the multiple locations;
obtaining spectroscopic data regarding the soil;
determining at least one of the group consisting of elevation, slope, surface curvature, relative topographic position, and topographic wetness index of the soil;
determining at least one of the group consisting of soil type, soil texture, and parent material type;
sampling a core of soil adjacent to the probe locations;
dividing the core into multiple depth increments;
analyzing the core;
matching each core with a corresponding depth increment of the probe insertions;
obtaining probe insertion 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, wherein the machine learning normalization algorithm is either a standard normal variate or a Savitzky-Golay algorithm;
standardizing the spectral variables to a common scale by removing a mean and scaling to unit variance;
reducing the number of spectral variables using a Recursive Feature Elimination algorithm with cross-validation and support vector regression;
generating all possible combinations of spectral normalization, regressors, and regressor parameters;
evaluating each of the combinations using five-fold cross validation;
choosing the combination yielding a lowest root mean square error of cross-validation; and
choosing a model utilizing a test set.