# US 2021/0240888 A1 · Assimilating a soil sample into a digital nutrient model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0240888 A1 - Document ID: US20210240888A1 - Status: Published application - Kind: A1 - Application number: 17/235,934 - Filed: 2021-04-20 - Published: 2021-08-05 - Earliest claimed: 2016-04-27 - Assignees: Climate LLC - Applicants: The Climate Corporation - Inventors: Wayne Tai Lee - Web page: https://patentgrep.com/patent/US20210240888A1 ## CPC - A01B 79/005: {Precision agriculture} - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - A01B 79/02: combined with other agricultural processing, e.g. fertilising, planting - G16B 15/00: ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment - A01C 21/007: {Determining fertilization requirements} - G01N 33/245: {for agricultural purposes} - G06Q 50/02: Agriculture; Fishing; Forestry; Mining - A01B: SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL (making or covering furrows or holes for sowing, planting or manuring A01C5/00; mowers convertible to soil working apparatus or capable of soil working A01D42/04; mowers combined with soil working implements A01D43/12; soil working for engineering purposes E01, E02, E21) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - A01C: PLANTING; SOWING; FERTILISING (parts, details or accessories of agricultural machines or implements, in general A01B51/00 - A01B75/00) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value. ## Claims ### Claim 1 A computer-implemented method comprising: storing, in digital memory of a computer system, a digital model for one or more properties of one or more fields over a period of time; receiving one or more digital measurement values specifying measurement of one or more properties at a particular field of the one or more fields at a particular time within the period of time; identifying, using the digital model and the one or more digital measurement values, a modeled property value representing a first estimate of the one or more properties of the particular field at the particular time; identifying an uncertainty value for the modeled property value, wherein the uncertainty value represents a magnitude of error in the digital model; and generating, based at least in part on the uncertainty value, an assimilated property value representing a second estimate of the one or more properties of the particular field at the particular time. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising displaying the assimilated property value. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: identifying one or more input uncertainties associated with the one or more digital measurement values; and identifying, based at least in part on the one or more input uncertainties, the uncertainty value for the modeled property value. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, further comprising: producing one or more perturbed modeling results by perturbing the one or more digital measurement values; and identifying, based at least in part on the one or more perturbed modeling results, the uncertainty value for the modeled property value. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: identifying one or more parameter uncertainties in one or more parameters of the digital model; and identifying, based at least in part on the one or more parameter uncertainties, the uncertainty value for the modeled property value. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, further comprising: producing one or more perturbed modeling results by perturbing the one or more parameters based on the one or more parameter uncertainties; and identifying, based at least in part on the one or more perturbed modeling results, the uncertainty value for the modeled property value. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising: identifying one or more structural uncertainties associated with physical interactions between one or more field properties and the modeled property value; and identifying, based at least in part on the one or more structural uncertainties, the uncertainty value for the modeled property value. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising calibrating, using the assimilated property value, the digital model to create a calibrated model of the one or more properties of one or more fields. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, further comprising identifying a set of perturbations of one or more of parameters or inputs to the digital model, the set of perturbations associated with a modeled property value that is closest to the assimilated property value. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, further comprising calibrating the digital model to the identified set of perturbations. ### Claim 11 A data processing system comprising: one or more processors; a memory storing instructions which, when executed by the or more processors, cause: storing a digital model for one or more properties of one or more fields over a period of time; receiving one or more digital measurement values specifying measurement of one or more properties at a particular field of the one or more fields at a particular time within the period of time; identifying, using the digital model and the one or more digital measurement values, a modeled property value representing a first estimate of the one or more properties of the particular field at the particular time; identifying an uncertainty value for the modeled property value, wherein the uncertainty value represents a magnitude of error in the digital model; and generating, based at least in part on the uncertainty value, an assimilated property value representing a second estimate of the one or more properties of the particular field at the particular time. ### Claim 12 (depends on 11) The data processing system of claim 11, wherein the instructions, when executed by the one or more processors, further cause displaying the assimilated property value. ### Claim 13 (depends on 11) The data processing system of claim 11, wherein the instructions, when executed by the one or more processors, further cause: identifying one or more input uncertainties associated with the one or more digital measurement values; and identifying, based at least in part on the one or more input uncertainties, the uncertainty value for the modeled property value. ### Claim 14 (depends on 13) The data processing system of claim 13, wherein the instructions, when executed by the one or more processors, further cause: producing one or more perturbed modeling results by perturbing the one or more digital measurement values; and identifying, based at least in part on the one or more perturbed modeling results, the uncertainty value for the modeled property value. ### Claim 15 (depends on 11) The data processing system of claim 11, wherein the instructions, when executed by the one or more processors, further cause: identifying one or more parameter uncertainties in one or more parameters of the digital model; and identifying, based at least in part on the one or more parameter uncertainties, the uncertainty value for the modeled property value. ### Claim 16 (depends on 15) The data processing system of claim 15, wherein the instructions, when executed by the one or more processors, further cause: producing one or more perturbed modeling results by perturbing the one or more parameters based on the one or more parameter uncertainties; and identifying, based at least in part on the one or more perturbed modeling results, the uncertainty value for the modeled property value. ### Claim 17 (depends on 11) The data processing system of claim 11, wherein the instructions, when executed by the one or more processors, further cause: identifying one or more structural uncertainties associated with physical interactions between one or more field properties and the modeled property value; and identifying, based at least in part on the one or more structural uncertainties, the uncertainty value for the modeled property value. ### Claim 18 (depends on 11) The data processing system of claim 11, wherein the instructions, when executed by the one or more processors, further cause calibrating, using the assimilated property value, the digital model to create a calibrated model of the one or more properties of one or more fields. ### Claim 19 (depends on 18) The data processing system of claim 18, wherein the instructions, when executed by the one or more processors, further cause identifying a set of perturbations of one or more of parameters or inputs to the digital model, the set of perturbations associated with a modeled property value that is closest to the assimilated property value. ### Claim 20 (depends on 19) The data processing system of claim 19, wherein the instructions, when executed by the one or more processors, further cause calibrating the digital model to the identified set of perturbations.